Skip to main content

Facial Emotion Expressions in Human–Robot Interaction: A Survey

Abstract

Facial expressions are an ideal means of communicating one’s emotions or intentions to others. This overview will focus on human facial expression recognition as well as robotic facial expression generation. In the case of human facial expression recognition, both facial expression recognition on predefined datasets as well as in real-time will be covered. For robotic facial expression generation, hand-coded and automated methods i.e., facial expressions of a robot are generated by moving the features (eyes, mouth) of the robot by hand-coding or automatically using machine learning techniques, will also be covered. There are already plenty of studies that achieve high accuracy for emotion expression recognition on predefined datasets, but the accuracy for facial expression recognition in real-time is comparatively lower. In the case of expression generation in robots, while most of the robots are capable of making basic facial expressions, there are not many studies that enable robots to do so automatically. In this overview, state-of-the-art research in facial emotion expressions during human–robot interaction has been discussed leading to several possible directions for future research.

Introduction

Robots are no longer just machines being used in factories and industries. There is a growing need and demand towards robots sharing space with humans as collaborative robotics or assistive robotics [35, 63]. Robots are, now, increasingly being deployed in a variety of domains as receptionists [120], educational tutors [49, 59], household supporters [111] and caretakers [25, 49, 67, 125]. Thus, there is a need for these social robots to effectively interact with humans, both verbally and non-verbally. Facial expressions are non-verbal signals that can be used to indicate one’s current status in a conversation, e.g., via backchanneling or rapport [3, 31].

Fig. 1
figure 1

Publications on emotion recognition of human faces during HRI and generation of facial expressions of robots

Perceived sociability is an important aspect in human–robot interaction (HRI) and users want robots to behave in a friendly and emotionally intelligent manner [28, 48, 99, 105]. For social robots to be more anthropomorphic and for human–robot interaction to be more like human-human interaction (HHI), robots need to be able to understand human emotions and appropriately respond to those human emotions. Stock and Merkle show that emotional expressions of anthropomorphic robots become increasingly important in business settings as well [118, 121]. The authors of [119] emphasize that robotic emotions are particularly important for the acceptance of a robot by the user. Thus, emotions are pivotal for HRI [122]. In any interaction, 7% of the affective information is conveyed through words, 38% is conveyed through tone, and 55% is conveyed through facial expressions [92]. This makes facial expressions an indispensable mode of affective communication. Accordingly, numerous studies have examined facial expressions of emotions during HRI [e.g. 18, 91, 8, 116, 15, 81, 2, 110, 17, 19, 33, 38, 50, 81, 91].

In any HHI, human beings first infer the emotional state of the other person and then accordingly generate facial expressions in response to their peer. The generated emotion could be a result of parallel empathy (generating the same emotion as the peer) or reactive empathy (generating emotion in response to the peer’s emotion) [26]. Similarly, in the case of HRI, we would like to study robots recognizing human emotion as well as robots generating their emotion as a response to human emotion.

There has been a growth in the number of papers on facial expressions in HRI in the last decade. Between 2000 and 2020 (see Fig. 1), there has been a gradual increase in the number of publications. Thus, the overarching research question is: What has been done so far on facial emotion expressions in human–robot interaction, and what still needs to be done?

Fig. 2
figure 2

Flowchart of the literature screening process

Fig. 3
figure 3

Framework of the overview

In Sect. 2 the framework of the overview is outlined, followed by the method of selection of studies in Sect. 3. Recognition of human facial expressions and generation of facial expressions by robots are covered in Sects. 4 and 5. The current state of the art and future research are discussed in Sect. 6 with the conclusion in Sect. 7.

Framework of the Overview

This overview focuses on two aspects: (1) recognition of human facial expressions and (2) generation of facial expressions by robots. The review framework (Fig. 3) is based on these two streams. (1) Recognition of human facial expressions is further subdivided depending on whether the recognition takes place on (a) a predefined dataset or in (b) real-time. (2) Generation of facial expressions by robots is also subdivided depending on whether the facial generation is (a) hand-coded or (b) automated, i.e., facial expressions of a robot are generated by moving the features (eyes, mouth) of the robot by hand-coding or automatically using machine learning techniques.

Method

Studies with the keywords “facial expression recognition AND human–robot interaction / HRI”, ”facial expression recognition” and ”facial expression generation AND human–robot interaction / HRI” between 2000 and 2020 were reviewed on Google Scholar.

In this overview, studies that use voice or body gestures as a modality for emotional expression but do not involve facial expressions are not included. Studies that involve HRI with humans having mental disorders like autism are also not included. Furthermore, studies that work on single emotion such as recognition of smile or facial expression generation of anger are not included. In total, 175 studies of 276 were rejected (Fig. 2).

Fig. 4
figure 4

Process of facial expression recognition in machine learning (adapted from Canedo and Neves [13])

Fig. 5
figure 5

Process of facial expression recognition in deep leaning (adapted from Li and Deng [71])

In Table 3, various studies on facial expression recognition are listed. Here, studies with an accuracy of greater than 90% for facial expression recognition on predefined datasets are selected. For real-time facial expression recognition, all studies that perform facial expression recognition in a human–robot interaction scenario are listed.

Recognition of Human Facial Expressions

Earlier, facial expression recognition (FER) consisted of the following steps: detection of face, image pre-processing, extraction of important features and classification of expression (Fig. 4). As deep learning algorithms have become popular, the pre-processed image is directly fed into deep networks (like CNN, RNN etc.) to predict an output [71] (Fig. 5).

In the machine learning algorithms, Viola Jones algorithm and OpenCV were popular choices for face detection. However, dlib face detector and ADABOOST algorithm were also used. To pre-process the images, greyscale conversion, image normalization, image augmentation (such as flip, zoom, rotate etc.) were usually applied. Further, some studies extract the important regions in faces like eyebrows, eyes, nose and mouth (also known as the acting units or AUs) that play an important role in FER. Others use local binary pattern (LBP) or histogram of oriented gradients (HOG) to extract the featural information. Finally, the classification is performed. Most of the studies perform classification for the six universally known emotions (happy, sad, disgust, anger, fear and surprise) and sometimes include a neutral expression. For final classification, k-Nearest Neighbor (KNN), Hidden Markov Model (HMM), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) are used.

In the deep learning algorithms, the input images are first pre-processed by performing face alignment, data augmentation and normalization. Then the images are directly fed into deep networks like CNN, RNN etc. which predict the emotion of the images. The most commonly used classification methods are explained in more detail below. They are arranged in the order in which they were invented.

KNN: Nearest neighbor based classifier was first invented in the 1950s [37]. In KNN [57], given the training instances and the class labels, the class label of an unknown instance is predicted. KNN is based on a distance function that measures the difference between two instances. While the Euclidean distance formula is mostly used, there are also other distance formulae such as Hamming distance which can be used.

HMM: An HMM [104] was introduced in the late 1960s. It is a doubly embedded stochastic process, bearing a hidden stochastic process (a Markov chain) that is only visible through another stochastic process, producing a sequence of observations. The state sequence can be learned using Viterbi algorithm or Expectation-Modification (EM) algorithm.

RNN: RNN [78] was introduced in the 1980s. RNN is a feed-forward neural network that has an edge over adjacent time steps, introducing a notion of time. Hence, RNN is mainly used for a dynamic data input that has a temporal sequence. In RNN, a state depends upon the current input as well as the state of the network at the previous time step, making it possible to contain information from a long time window.

CNN: Convolutional Networks [70] were invented in 1989 [69]. CNNs are trainable multistage architectures composed of multiple stages. The input and output of each stage are sets of arrays called feature maps. Each stage of CNN is composed of three layers- a filter bank layer, a non-linearity layer and a feature pooling layer. The network is trained using the backpropagation method. They are used for end-to-end recognition wherein given the input image, the output is predicted by CNNs. They are even used as feature extractors which are further connected with neural networks layers like LSTM or RNN for the prediction.

SVM: SVM was invented by Vapnik [128]. In SVM [129], the training data can be separated by a hyperplane.

LSTM: LSTM [39] was invented by Hochreiter and Schmidhuber [47]. It also has recurrent connections but unlike RNN, it is capable of learning long-term dependencies.

Table 1 summarizes the major purpose, application areas, advantages, disadvantages and frequency of use for commonly used algorithms. For the frequency of use, only the number of papers that implement facial expression recognition during HRI or in real-time scenarios were counted. Although RNN has not been used for facial expression recognition during HRI or in real-time, some studies perform facial expression recognition on predefined datasets using RNN.

27 studies on facial expression recognition during HRI were reviewed. Some of the studies have not been performed on a robot platform. These studies perform emotion recognition in real-time and mention HRI as their intended application. The studies are summarized in Table 2. Here, studies that perform facial expression recognition on predefined datasets or studies that perform facial expression recognition but not in real-time were not included.

Table 1 Details about the commonly used algorithms
Table 2 Detailed information about studies on emotion recognition and Human Robot Interaction (HRI)
Table 3 Studies on FER; Note: Studies listed according to accuracy level

FER on Predefined Dataset

Fig. 6
figure 6

Facial expression generation techniques

Although the goal of this study is to perform FER in real-time and during HRI, the studies on real-time FER are compared with FER on predefined datasets. FER has been carried out on static human images as well as on dynamic human video clips. While some studies, perform facial recognition on still images, others perform facial recognition on videos. In Datcu and Rothkrantz [24], they show that there is an advantage in using data from video frames over still images. This is because videos contain temporal information that is absent in still images.

Results of studies with above 90% accuracy in FER on still images are summarized in Table 3a and on videos are summarized in Table 3b. Table 3a, b are for comparison with Table 3c. Studies are arranged according to their accuracy level. It should be noted that these studies are carried out on predefined datasets consisting of human images and videos and do not involve robots. There are a considerable number of studies that achieve accuracy greater than 90% on CK+, Jaffe and Oulu-Casia datasets on both still images and videos.

FER in Real-Time

It is easier to achieve high accuracy while performing emotion recognition on predefined datasets as they are recorded under controlled environmental conditions. On the other hand, it is difficult to achieve the same level of accuracy when performing emotion recognition in real-time when the movements are spontaneous. It should be noted that studies that perform facial expression recognition in real-time were carried out under controlled laboratory conditions with little variation in lighting conditions and head poses.

As this study is about facial expressions in HRI, for a robot to be able to recognize emotion, emotion recognition has to be performed in real-time. Table 3c provides studies with facial expression recognition in real-time for HRI. Here, the accuracies are comparatively lower than the accuracies for predefined datasets. As can be seen in Table 3c, only two studies have an accuracy greater than 90%. The robots that are used in the studies are either robotic heads or humanoid robots such as Pepper, Nao, iCub etc. Many studies that perform facial expression recognition in real-time use CNNs, making it a popular choice for facial expression recognition [2, 2, 8, 15, 133]. However, the highest accuracy is achieved by Bayesian and Artificial Neural Network (ANN) methods for facial expression recognition in real-time.

Table 4 Detailed information about studies on emotion expression and Human Robot Interaction (HRI)

Facial Emotion Expression by Robots

For robots to be empathic, it is necessary that the robots not only be able to recognize human emotions but also be able to generate emotions using facial expressions. Several studies enable robots to generate facial expressions either in a hand-coded or an automated manner (Fig. 6). By hand-coded, we mean that the facial expressions are coded by moving the eyes and mouth of the robot in a desirous manner, and automated is when the emotions are learned automatically using machine learning techniques.

16 studies on facial emotion expression in robots were reviewed. These studies are summarized in Table 4.

Facial Expression Generation is Hand-Coded

Earlier studies started by hand-coding the facial expressions in robots. There is a static as well as dynamic generation of facial expressions on robots.

Among the static methods, there is a humanoid social robot “Alice” that imitates human facial expressions in real-time [91]. Kim et al. [61] introduced an artificial facial expression imitation system using a robot head, Ulkni. As Ulkni is composed of 12 RC servos, with four Degrees of Freedom (DoFs) to control its gaze direction, two DoFs for its neck, and six DoFs for its eyelids and lips, it is capable of making the basic facial expressions after the position commands for actuators are sent from the PC. Bennett and Sabanovic [7] identified minimal features, i.e. movement of eyes, eyebrows, mouth and neck, which are sufficient to identify the facial expression.

In this study, the main program called functions that specified facial expressions according to the direction (used to make or undo an expression) and degree (strength of the expression–i.e. smaller vs. larger). The facial expression functions would in turn call lower functions that moved specific facial components given a direction and degree, following the movement related to specific AUs in the facial acting coding system (FACS).

Breazeal’s [9] robot Kismet generated emotions using an interpolation-based technique over a 3-D space, where the three dimensions correspond to valence, arousal and stance. The expressions become intense as the affect state moves to extreme values in the affect space. Park et al. [102] made diverse facial expressions by changing their dynamics and increased the lifelikeness of a robot by adding secondary actions such as physiological movements (eye blinking and sinusoidal motions concerning respiration). A second-order differential equation based on the linear affect-expressions space model is used to achieve the dynamic motion for expressions. Prajapati et al. [103] used a dynamic emotion generation model to convert the facial expressions derived from the human face into a more natural form before rendering them on the robotic face. The model is provided with the facial expression of the person interacting with the system and corresponding synthetic emotions generated are fed to the robotic face.

Summary of findings The robot faces are capable of making basic facial expressions as they contain enough DoFs in the eyes and mouth. They are able to generate static emotions [7, 61, 91]. Additionally, the robot faces are able to generate dynamic emotions [9, 102, 103].

Facial Expression Generation is Automated

Some of the studies automatically generate facial expressions on robots. Unlike hand-coded techniques where the commands for the position of features like eyes and mouth are sent from the computer, here, the facial expressions are generated using machine learning techniques such as neural networks and RL.

Breazeal et al. [10] presented a robot Leonardo that can imitate human facial expressions. They use neural networks to learn the direct mapping of a human’s facial expressions onto Leonardo’s own joint space. In Horii et al. [50], the robot does not directly imitate the human but estimates the correct emotion and generates the estimated emotion using RBM. RBM [46] is a generative model that represents the generative process of data distribution and latent representation, and can generate data from latent signals [98, 117, 123].

Li and Hashimoto [73] developed a KANSEI communication system based on emotional synchronization. KANSEI is a Japanese term that means emotions, feeling, sensitivity etc. The KANSEI communication system first recognizes human emotion and maps the recognized emotion to the emotion generation space. Finally, the robot expresses its emotion synchronized with the human’s emotion in the emotion generation space. When the human changes his/her emotion, the robot also synchronizes its emotion with the human’s emotion, establishing a continuous communication between the human and the robot. It was found that the subjects became more comfortable with the robot and communicated more with the robot when there was emotional synchronization.

In Churamani et al. [17], the robot Nico learned the correct combination of eyebrow and mouth wavelet parameters to express its mood using RL. The learned expressions looked slightly distorted but were sufficient to distinguish between various expressions. The robot could also generate expressions that were not limited to the basic five expressions that were learned. For a mixed emotional state (for example, anger mixed with sadness), the model was able to generate novel expression representations representing the mixed state of the mood.

Summary of findings In all of the above studies, the robots learn to generate facial expressions automatically using machine learning techniques. While Breazeal [10], Li and Hashimoto [73] used direct mapping of human facial expressions, Horii et al. [50] generated the estimated human’s emotion on the robot. In Churamani et al. [17], the robot was able to associate the learned expressions with the context of the conversation.

Table 5 Possible categories for facial recognition in the wild

Discussion

Summary of the State of the Art

There are already studies having high accuracy (greater than 90%) in facial expression recognition on CK+, Jaffe and Oulu-Casia datasets. (see Table 3a, b). The accuracies on CK+, Jaffe and Oulu-Casia datasets have been as high as 100%, 99.8% and 92.89% respectively. In comparison to this, the accuracy for facial expression recognition in real-time is not as high.

Zhang et al. [146] used a deep convolutional network (DCN) that had an accuracy of 98.9% on CK+ dataset and 55.27% on Static Facial Expressions in the Wild (SFEW) dataset. Here, the same network produced very different results for two different datasets. SFEW [29] consists of close to a real-world environment extracted from movies. The database covers unconstrained facial expressions, varied head poses, large age range, occlusions, varied focus, different resolution of faces, and close to real-world illumination. In Zhang et al. [146] the accuracy for ”in the wild” settings was considerably lower than on CK+ dataset, implying that the expression recognition algorithms can still not handle the variations in environment, head poses etc. in real-world settings.

Table 5 provides possible categories for facial recognition in the wild. It contains the basic emotional facial expressions, situation-specific face occlusions, permanent face features, face movements, situation-specific expressions and side activities during facial expressions.

Most of the current research in facial expression recognition relates to the first category of basic emotional facial expression. Survey articles on facial expression recognition have been cited in the Table 5 [11, 13, 21, 42, 43, 71, 88, 109, 112]. For more details on individual studies, refer to Table 3. Facial expression recognition in the presence of situation-specific face occlusions like a mouth–nose mask, glasses, hand in front of face etc. has also been studied [74, 75, 131]. Pose invariant facial expression recognition when the face is moving or turned sideways has also been partially studied [96, 113, 143, 144].

For the facial expression generation, robots can make certain basic facial expressions by moving their eyes, mouth and neck. However, they cannot make as many expressions as human beings due to the limited number of DoFs present in a robot’s face. There are relatively fewer studies for automated facial expression generation in robots [10, 17, 50, 73]. While the robots are capable of displaying their facial expressions by manually coding the movement of the eyes and mouth, there are fewer studies that would make a robot learn to display its facial expressions automatically.

Most of the studies on facial expression generation have been carried out on robotic heads or humanoid robots like iCub and Nico [e.g. 9, 10, 17, 50]. In Becker-Asano and Ishiguro [5], Geminoid F’s facial actuators are tuned such that the readability of its facial expressions is comparable to a real person’s static display of emotional expression. It was found that the android’s emotional expressions were more ambiguous than that of a real person and ’fear’ was often confused with ’surprise’.

An advantage of automated facial expression generation over hand-coded facial expression generation is that in automated facial expression generation, a robot could learn mixed expressions than simply the learned expressions. Unlike in hand-coded facial expression generation, where a robot can only express the emotions that it has learned, in Churamani et al. [17], the robot could express complex emotions that were made up of a combination of emotions.

Future Research

Although facial expression recognition under specific settings has high accuracy and robots can express basic emotions through facial expressions, there are several possible directions for future research in this area.

Suggestion 1: Performing facial expression recognition in the wild needs to be emphasized upon.

To efficiently recognize facial expressions in real-time and in a real-world environment, the robot should be able to perform facial expression recognition with varied head poses, varied focus, presence of occlusions, different resolutions of the face and varied illumination conditions. The studies that perform facial expression recognition in real-time are limited to a laboratory environment which is far different from a real-world scenario. A good study would be the one where facial expression recognition in the wild is performed.

Some studies perform facial expression recognition in the wild, but their accuracy is much less than the accuracy on predefined datasets like CK+, Jaffe, MMI etc. To increase the efficiency of facial expression recognition in real-world scenarios, the performance of facial expression recognition in the wild needs to be improved. This can also be used to recognize facial expressions in real-time. Based on this, a direct adaptation of emotions would make HRI smoother.

Suggestion 2: Facial expressions during activities like talking, nodding etc. need to be studied.

Situation-specific expressions (nodding, yawning, blinking, looking down) and side activities during facial expressions (talking, eating, drinking, sneezing) in Table 5 have not been studied. To understand vivid expressions, it is required to be able to recognize facial expressions for all categories. Humans also express emotions while interacting with someone verbally, such as smiling while speaking when they are happy. In this case, it should be possible to recognize a smile during speech.

Suggestion 3: Combine facial expression recognition with the data from other modalities such as voice, text, body gestures and physiological data to improve the emotion recognition rate.

Although this overview focuses on facial expression recognition, it may be possible to control one’s face and not express the emotion one is truly experiencing. Some studies combine facial expression recognition with audio data, body gestures or physiological data for an improved emotion recognition [41, 53, 83]. There are very few studies that combine facial data with both audio and physiological data [106, 107] and studies that analyze all modalities (face, voice, text, body gestures and physiological signals) have not been found. Humans can recognize the emotion of a person quickly and effectively by taking into account their facial expression, body gestures, voice and words. Combining facial, audio, text and body gestures with physiological data could lead to a higher emotion recognition rate by machine learning algorithms than by humans.

Suggestion 4: How should a robot react towards a given human emotion?

In HHI, a human’s reaction to a given emotion is either a result of parallel empathy or reactive empathy [26]. It should be studied with which emotion should a robot appropriately react to a given human emotion. Moreover, it needs to be studied if a robot should be able to express negative emotions. Most of the existing studies allow a robot to be able to express basic emotions (anger, fear, happiness, neutral, sadness, surprise). It may be reasonable for a robot to react with a sad expression when a human being expresses anger. But, should a robot be able to express extreme emotions such as anger?

For facial expression generation, while robots are capable of displaying facial expressions both static and dynamic, they are unable to generate facial expressions when they are speaking. For example, robots could smile while talking to express their happiness or they could speak with a frown when angry. Robots could also express their emotions through partial facial or bodily gestures instead of showing a full face expression. For example, tilting head down to express sadness, frowning to express anger, eyes wide open to express surprise and raising eyebrows.

Suggestion 5: Robots should be able to recognize and generate facial expressions with various intensities.

Emotions form a continuous range and can have various intensities. If one is less happy, one would smile less. Similarly, if someone is very happy, the smile would also be big. It should be possible to recognize not just the emotion but the intensity of emotion. Moreover, in most of the existing studies, robots express their emotions with only one configuration per emotion. Robots should also be able to express their emotions with different intensities. Finally, it needs to be studied whether the intensity of emotion with which a robot reacts to a given human emotion has any effects on the human and whether these effects are positive or negative.

Suggestion 6: Robots should be able to express their emotions through a combination of body gestures and facial expressions.

While in this overview, we focus on robotic facial expressions, there are other articles where emotional expression is performed through the robot’s body postures [4, 20, 22, 55, 86, 90]. A potential future study could be to compare the robot’s facial expressions with robot’s bodily expressions and also with the combination of facial and bodily expressions to see if there is any difference in the recognition of these.

Suggestion 7: Robots should be able to both recognize and generate complex emotions such as that of thinking, calm and bored states.

For both facial expression recognition and generation, there is a need to go beyond the basic seven emotions to recognizing and generating more complex emotions such as calm, fatigued, bored etc. It might be difficult to generate complex emotions given the hardware limitations of the robot, but if this is made possible, robots could express a wider range of emotions similar to human beings.

Conclusion

This overview emphasizes the recognition of human facial expressions and the generation of robotic facial expressions. There are already plenty of studies having high accuracy for facial expression recognition on pre-existing datasets. Accuracy on facial expression recognition in the wild is considerably lower than the experiments which have been conducted under controlled laboratory conditions. For human facial emotion recognition, future work would be to improve emotion recognition for non-frontal head poses in presence of occlusions (i.e. emotion recognition in the wild). It should be made possible to recognize emotions during speech as well emotions with varying intensities. In the case of facial expression generation in robots, robots are capable of making the basic facial expressions. Few studies perform autonomous facial generation in robots. In the future, there could be studies comparing robotic facial expressions with the robot’s bodily expressions and also with a combination of facial and bodily expressions to see if there is any difference in recognizing these. Robots should be able to express their emotion with partial bodily or facial gestures while speaking. They should also be express their emotions with various intensities instead of a single configuration per emotion. Lastly, there is a need to go beyond the basic seven expressions for both facial expression recognition and generation.

References

  1. Ahmed TU, Hossain S, Hossain MS, ul Islam R, Andersson K (2019) Facial expression recognition using convolutional neural network with data augmentation. In: 2019 joint 8th international conference on informatics, electronics vision (ICIEV) and 2019 3rd international conference on imaging, vision pattern recognition (icIVPR), pp 336–341

  2. Barros P, Weber C, Wermter S (2015) Emotional expression recognition with a cross-channel convolutional neural network for human–robot interaction. In: 2015 IEEE-RAS 15th international conference on humanoid robots (humanoids), pp 582–587

  3. Bavelas J, Gerwing J (2011) The listener as addressee in face-to-face dialogue. Int J Listen 25:178–198

    Article  Google Scholar 

  4. Beck A, Cañamero L, Hiolle A, Damiano L, Cosi P, Tesser F, Sommavilla G (2013) Interpretation of emotional body language displayed by a humanoid robot: a case study with children. Int J Soc Robot 5(3):325–334

    Article  Google Scholar 

  5. Becker-Asano C, Ishiguro H (2011) Evaluating facial displays of emotion for the android robot geminoid f, pp 1–8

  6. Bengio Y, Simard P, Frasconi P (1994) Learning long-term dependencies with gradient descent is difficult. IEEE Trans Neural Netw 5(2):157–166

    Article  Google Scholar 

  7. Bennett CC, Sabanovic S (2014) Deriving minimal features for human-like facial expressions in robotic faces. Int J Soc Robot 6:367–381

    Article  Google Scholar 

  8. Bera A, Randhavane T, Prinja R, Kapsaskis K, Wang A, Gray K, Manocha D (2019) The emotionally intelligent robot: improving social navigation in crowded environments. ArXiv arXiv:1903.03217

  9. Breazeal C (2003) Emotion and sociable humanoid robots. Int J Hum Comput Stud 59(1–2):119–155

    Article  Google Scholar 

  10. Breazeal C, Buchsbaum D, Gray J, Gatenby D, Blumberg B (2005) Learning from and about others: towards using imitation to bootstrap the social understanding of others by robots. Artif Life 11:31–62. https://doi.org/10.1162/1064546053278955

    Article  Google Scholar 

  11. Buciu I, Kotsia I, Pitas I (2005) Facial expression analysis under partial occlusion, pp v/453 –v/456, vol 5

  12. Byeon YH, Kwak KC (2014) Facial expression recognition using 3d convolutional neural network. Int J Adva Comput Sci Appl 5(12)

  13. Canedo D (2019) Facial expression recognition using computer vision: a systematic review. Appl Sci. https://doi.org/10.3390/app9214678

    Article  Google Scholar 

  14. Carcagnì P, Del Coco M, Leo M, Distante C (2015) Facial expression recognition and histograms of oriented gradients: a comprehensive study. Springerplus 4(1):645

    Article  Google Scholar 

  15. Chen H, Gu Y, Wang F, Sheng W (2018) Facial expression recognition and positive emotion incentive system for human–robot interaction. In: 2018 13th world congress on intelligent control and automation (WCICA), pp 407–412

  16. Chen X, Yang X, Wang M, Zou J (2017) Convolution neural network for automatic facial expression recognition. In: 2017 international conference on applied system innovation (ICASI), pp 814–817

  17. Churamani N, Barros P, Strahl E, Wermter S (2018) Learning empathy-driven emotion expressions using affective modulations

  18. Cid F, Moreno J, Bustos P, Núñez P (2014) Muecas: a multi-sensor robotic head for affective human robot interaction and imitation. Sensors (Basel, Switzerland) 14:7711–7737

    Article  Google Scholar 

  19. Cid F, Prado JA, Bustos P, Núñez P (2013) A real time and robust facial expression recognition and imitation approach for affective human–robot interaction using gabor filtering. In: 2013 IEEE/RSJ international conference on intelligent robots and systems, pp 2188–2193 . https://doi.org/10.1109/IROS.2013.6696662

  20. Cohen I (2010) Recognizing robotic emotions: facial versus body posture expression and the effects of context and learning. Master’s thesis

  21. Corneanu CA, Simón MO, Cohn JF, Guerrero SE (2016) Survey on rgb, 3d, thermal, and multimodal approaches for facial expression recognition: history, trends, and affect-related applications. IEEE Trans Pattern Anal Mach Intell 38(8):1548–1568

    Article  Google Scholar 

  22. Costa S, Soares F, Santos C (2013) Facial expressions and gestures to convey emotions with a humanoid robot. In: International conference on social robotics. Springer, pp 542–551

  23. Dandıl E, Özdemir R (2019) Real-time facial emotion classification using deep learning. Data Sci Appl 2(1):13–17

    Google Scholar 

  24. Datcu D, Rothkrantz L (2007) Facial expression recognition in still pictures and videos using active appearance models. A comparison approach, p 112. https://doi.org/10.1145/1330598.1330717

  25. Dautenhahn K (2007) Methodology & themes of human–robot interaction: a growing research field. Int J Adv Robot Syst 4:15

    Article  Google Scholar 

  26. Davis M (2018) Empathy: a social psychological approach

  27. Deng J, Pang G, Zhang Z, Pang Z, Yang H, Yang G (2019) cgan based facial expression recognition for human–robot interaction. IEEE Access 7:9848–9859

    Article  Google Scholar 

  28. de Graaf M, Allouch S, Van Dijk JA (2016) Long-term acceptance of social robots in domestic environments: insights from a user’s perspective

  29. Dhall A, Goecke R, Lucey S, Gedeon T (2011) Static facial expression analysis in tough conditions: data, evaluation protocol and benchmark. In: 2011 IEEE international conference on computer vision workshops (ICCV Workshops), pp 2106–2112

  30. Ding H, Zhou SK, Chellappa R (2017) Facenet2expnet: regularizing a deep face recognition net for expression recognition. In: 2017 12th IEEE international conference on automatic face gesture recognition (FG 2017), pp 118–126

  31. Drolet A, Morris MW (2000) Rapport in conflict resolution: accounting for how face-to-face contact fosters mutual cooperation in mixed-motive conflicts. J Exp Soc Psychol 36:26–50

    Article  Google Scholar 

  32. Elaiwat S, Bennamoun M, Boussaïd F (2016) A spatio-temporal rbm-based model for facial expression recognition. Pattern Recogn 49:152–161

    Article  Google Scholar 

  33. Esfandbod A, Rokhi Z, Taheri A, Alemi M, Meghdari A (2019) Human–robot interaction based on facial expression imitation. In: 2019 7th international conference on robotics and Mechatronics (ICRoM), pp 69–73

  34. Faria DR, Vieira M, Faria FCC, Premebida C (2017) Affective facial expressions recognition for human–robot interaction. In: 2017 26th IEEE international symposium on robot and human interactive communication (RO-MAN), pp 805–810

  35. Feil-Seifer D, Matarić MJ (2011) Socially assistive robotics. IEEE Robot Autom Mag 18(1):24–31

    Article  Google Scholar 

  36. Ferreira PM, Marques F, Cardoso JS, Rebelo A (2018) Physiological inspired deep neural networks for emotion recognition. IEEE Access 6:53930–53943

    Article  Google Scholar 

  37. Fix E (1951) Discriminatory analysis: nonparametric discrimination, consistency properties. USAF School of Aviation Medicine

  38. Ge S, Wang C, Hang C (2008) A facial expression imitation system in human robot interaction

  39. Gers FA, Schmidhuber J (2000) Recurrent nets that time and count. In: Proceedings of the IEEE-INNS-ENNS international joint conference on neural networks. IJCNN 2000. Neural computing: new challenges and perspectives for the new millennium. IEEE, vol 3, pp 189–194

  40. Gogić I, Manhart M, Pandžić I, Ahlberg J (2018) Fast facial expression recognition using local binary features and shallow neural networks. Vis Comput. https://doi.org/10.1007/s00371-018-1585-8

    Article  Google Scholar 

  41. Gunes H, Piccardi M (2007) Bi-modal emotion recognition from expressive face and body gestures. J Netw Comput Appl 30(4):1334–1345

    Article  Google Scholar 

  42. Gunes H, Schuller B (2013) Categorical and dimensional affect analysis in continuous input: current trends and future directions. Image Vis Comput 31:120–136

    Article  Google Scholar 

  43. Gunes H, Schuller B, Pantic M, Cowie R (2011) Emotion representation, analysis and synthesis in continuous space: a survey. Face Gesture 2011:827–834

    Google Scholar 

  44. Hamester D, Barros P, Wermter S (2015) Face expression recognition with a 2-channel convolutional neural network, pp 1–8 . https://doi.org/10.1109/IJCNN.2015.7280539

  45. Hazar M, Fendri E, Hammami M (2015) Face recognition through different facial expressions. J Signal Process Syst. https://doi.org/10.1007/s11265-014-0967-z

    Article  Google Scholar 

  46. Hinton G, Salakhutdinov R (2006) Reducing the dimensionality of data with neural networks. Science (New York, NY) 313:504–7. https://doi.org/10.1126/science.1127647

    MathSciNet  Article  MATH  Google Scholar 

  47. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    Article  Google Scholar 

  48. Hoffman G, Breazeal C (2006) Robotic partners’ bodies and minds: an embodied approach to fluid human–robot collaboration. In: AAAI workshop—technical report

  49. Hoffman G, Zuckerman O, Hirschberger G, Luria M, Shani Sherman T (2015) Design and evaluation of a peripheral robotic conversation companion. In: Proceedings of the Tenth Annual ACM/IEEE international conference on human–robot interaction, HRI ’15, pp 3–10. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/2696454.2696495

  50. Horii T, Nagai Y, Asada M (2016) Imitation of human expressions based on emotion estimation by mental simulation. Paladyn J Behav Robot. https://doi.org/10.1515/pjbr-2016-0004

    Article  Google Scholar 

  51. Hossain MS, Muhammad G (2017) An emotion recognition system for mobile applications. IEEE Access 5:2281–2287

    Article  Google Scholar 

  52. Hua W, Dai F, Huang L, Xiong J, Gui G (2019) Hero: human emotions recognition for realizing intelligent internet of things. IEEE Access 7:24321–24332

    Article  Google Scholar 

  53. Huang Y, Yang J, Liao P, Pan J (2017) Fusion of facial expressions and eeg for multimodal emotion recognition. Comput Intell Neurosci 2017:1–8. https://doi.org/10.1155/2017/2107451

    Article  Google Scholar 

  54. Ilic, D., Žužić, I., Brscic, D.: Calibrate my smile: robot learning its facial expressions through interactive play with humans, pp 68–75 (2019)

  55. Inthiam J, Hayashi E, Jitviriya W, Mowshowitz A (2019) Mood estimation for human–robot interaction based on facial and bodily expression using a hidden Markov model. In: 2019 IEEE/SICE international symposium on system integration (SII). IEEE, pp 352–356

  56. Inthiam J, Mowshowitz A, Hayashi E (2019) Mood perception model for social robot based on facial and bodily expression using a hidden Markov model. J Robot Mechatron 31:629–638

    Article  Google Scholar 

  57. Jiang L, Cai Z, Wang D, Jiang S (2007) Survey of improving k-nearest-neighbor for classification. In: Fourth international conference on fuzzy systems and knowledge discovery (FSKD 2007), vol 1, pp 679–683

  58. Kabir MH, Salekin MS, Uddin MZ, Abdullah-Al-Wadud M (2017) Facial expression recognition from depth video with patterns of oriented motion flow. IEEE Access 5:8880–8889

    Article  Google Scholar 

  59. Kanda T, Hirano T, Eaton D, Ishiguro H (2004) Interactive robots as social partners and peer tutors for children: a field trial. Hum Comput Interact (Special issues on human–robot interaction) 19:61–84

    Google Scholar 

  60. Kar NB, Babu KS, Jena SK (2017) Face expression recognition using histograms of oriented gradients with reduced features. In: Raman B, Kumar S, Roy PP, Sen D (eds) Proceedings of international conference on computer vision and image processing. Springer, Singapore, pp 209–219

  61. Kim DH, Jung S, An K, Lee H, Chung M (2006) Development of a facial expression imitation system, pp 3107–3112

  62. Kim J, Kim B, Roy PP, Jeong D (2019) Efficient facial expression recognition algorithm based on hierarchical deep neural network structure. IEEE Access 7:41273–41285

    Article  Google Scholar 

  63. Kirgis FP, Katsos P, Kohlmaier M (2016) Collaborative robotics. Springer, Cham, pp 448–453

    Google Scholar 

  64. Kishi T, Otani T, Endo N, Kryczka P, Hashimoto K, Nakata K, Takanishi A (2012) Development of expressive robotic head for bipedal humanoid robot. In: 2012 IEEE/RSJ international conference on intelligent robots and systems, pp 4584–4589

  65. Kotsia I, Nikolaidis N, Pitas I (2007) Facial expression recognition in videos using a novel multi-class support vector machines variant. In: 2007 IEEE international conference on acoustics, speech and signal processing–ICASSP ’07, vol 2, pp II-585–II-588

  66. Kotsia I, Pitas I (2007) Facial expression recognition in image sequences using geometric deformation features and support vector machines. IEEE Trans Image Process 16(1):172–187

    MathSciNet  Article  Google Scholar 

  67. Kozima H, Nakagawa C, Yasuda Y (2005) Interactive robots for communication-care: a case-study in autism therapy. In: ROMAN 2005. In: IEEE international workshop on robot and human interactive communication, pp 341–346

  68. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444

    Article  Google Scholar 

  69. LeCun Y, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W, Jackel LD (1989) Backpropagation applied to handwritten zip code recognition. Neural Comput 1(4):541–551

    Article  Google Scholar 

  70. LeCun Y, Kavukcuoglu K, Farabet C (2010) Convolutional networks and applications in vision. In: Proceedings of 2010 IEEE international symposium on circuits and systems, pp 253–256

  71. Li S, Deng W (2020) Deep facial expression recognition: a survey. IEEE Trans Affect Comput, pp 1–1

  72. Li TS, Kuo P, Tsai T, Luan P (2019) Cnn and lstm based facial expression analysis model for a humanoid robot. IEEE Access 7:93998–94011

    Article  Google Scholar 

  73. Li Y, Hashimoto M (2011) Effect of emotional synchronization using facial expression recognition in human–robot communication

  74. Li Y, Zeng J, Shan S, Chen X (2018) Occlusion aware facial expression recognition using cnn with attention mechanism. IEEE Trans Image Process, pp 1–1 (2018)

  75. Li Y, Zeng J, Shan S, Chen X (2018) Patch-gated cnn for occlusion-aware facial expression recognition. In: 2018 24th international conference on pattern recognition (ICPR), pp 2209–2214

  76. Liang D, Liang H, Yu Z, Zhang Y (2019) Deep convolutional bilstm fusion network for facial expression recognition. Vis Comput 36:499–508

    Article  Google Scholar 

  77. Liliana DY, Basaruddin C, Widyanto MR (2017) Mix emotion recognition from facial expression using svm-crf sequence classifier. In: Proceedings of the international conference on algorithms, computing and systems, ICACS ’17. Association for Computing Machinery, New York, NY, USA, pp 27–31

  78. Lipton ZC, Berkowitz J, Elkan C (2015) A critical review of recurrent neural networks for sequence learning. arXiv preprint arXiv:1506.00019

  79. Liu K, Hsu C, Wang W, Chiang H (2019) Real-time facial expression recognition based on cnn. In: 2019 international conference on system science and engineering (ICSSE), pp 120–123 . https://doi.org/10.1109/ICSSE.2019.8823409

  80. Liu P, Choo KKR, Wang L, Huang F (2017) Svm or deep learning? A comparative study on remote sensing image classification. Soft Comput 21(23):7053–7065

    Article  Google Scholar 

  81. Liu ZT, Wu M, Cao W, Chen LF, Xu J, Zhang R, Zhou M, Mao J (2017) A facial expression emotion recognition based human–robot interaction system. IEEE/CAA J Autom Sin 4:668–676. https://doi.org/10.1109/JAS.2017.7510622

    Article  Google Scholar 

  82. Lopez-Rincon A (2019) Emotion recognition using facial expressions in children using the nao robot. In: 2019 international conference on electronics, communications and computers (CONIELECOMP), pp 146–153

  83. Ma F, Zhang W, Li Y, Huang SL, Zhang L (2020) Learning better representations for audio-visual emotion recognition with common information. Appl Sci 10:7239. https://doi.org/10.3390/app10207239

    Article  Google Scholar 

  84. Maeda Y, Geshi S (2018) Human–robot interaction using Markovian emotional model based on facial recognition. In: 2018 Joint 10th international conference on soft computing and intelligent systems (SCIS) and 19th international symposium on advanced intelligent systems (ISIS). IEEE, pp 209–214

  85. Mannan MA, Lam A, Kobayashi Y, Kuno Y (2015) Facial expression recognition based on hybrid approach. In: Huang DS, Han K (eds) Adv Intell Comput Theor Appl. Springer, Cham, pp 304–310

    Google Scholar 

  86. Marmpena M, Lim A, Dahl TS, Hemion N (2019) Generating robotic emotional body language with variational autoencoders. In: 2019 8th international conference on affective computing and intelligent interaction (ACII). IEEE, pp 545–551

  87. Martin C, Werner U, Gross H (2008) A real-time facial expression recognition system based on active appearance models using gray images and edge images. In: 2008 8th IEEE international conference on automatic face gesture recognition, pp 1–6 . https://doi.org/10.1109/AFGR.2008.4813412

  88. Martinez B, Valstar MF, Jiang B, Pantic M (2019) Automatic analysis of facial actions: a survey. IEEE Trans Affect Comput 10(3):325–347

    Article  Google Scholar 

  89. Mayya V, Pai RM, Pai MMM (2016) Automatic facial expression recognition using DCNN. Proc Comput Sci 93:453–461. https://doi.org/10.1016/j.procs.2016.07.233

    Article  Google Scholar 

  90. McColl D, Nejat G (2014) Recognizing emotional body language displayed by a human-like social robot. Int J Soc Robot 6(2):261–280

    Article  Google Scholar 

  91. Meghdari A, Shouraki S, Siamy A, Shariati A (2016) The real-time facial imitation by a social humanoid robot

  92. Mehrabian A (1968) Communication without words. Psychol Today 2:53–56

    Google Scholar 

  93. Meng Z, Liu P, Cai J, Han S, Tong Y (2017) Identity-aware convolutional neural network for facial expression recognition. In: 2017 12th IEEE international conference on automatic face gesture recognition (FG 2017), pp 558–565 (2017)

  94. Minaee S, Abdolrashidi A (2019) Deep-emotion: facial expression recognition using attentional convolutional network. arXiv preprint arXiv:1902.01019

  95. Mistry K, Zhang L, Neoh SC, Lim CP, Fielding B (2017) A micro-GA embedded PSO feature selection approach to intelligent facial emotion recognition. IEEE Trans Cybern 47(6):1496–1509

    Article  Google Scholar 

  96. Moeini A, Moeini H, Faez K (2014) Pose-invariant facial expression recognition based on 3d face reconstruction and synthesis from a single 2d image. In: 2014 22nd international conference on pattern recognition, pp 1746–1751. https://doi.org/10.1109/ICPR.2014.307

  97. Mollahosseini A, Chan D, Mahoor MH (2016) Going deeper in facial expression recognition using deep neural networks. In: 2016 IEEE winter conference on applications of computer vision (WACV), pp 1–10 (2016)

  98. Ngiam J, Khosla A, Kim M, Nam J, Lee H, Ng AY (2011) Multimodal deep learning. In: ICML

  99. Nicolescu MN, Mataric MJ (2001) Learning and interacting in human–robot domains. IEEE Trans Syst Man Cybern Part A Syst Hum 31(5):419–430

    Article  Google Scholar 

  100. Nunes ARV (2019) Deep emotion recognition through upper body movements and facial expression. Master’s thesis, Aalborg University

  101. Nwosu L, Wang H, Lu J, Unwala I, Yang X, Zhang T (2017) Deep convolutional neural network for facial expression recognition using facial parts. In: 2017 IEEE 15th international conference on dependable, autonomic and secure computing, 15th international conference on pervasive intelligence and computing, 3rd international conference on big data intelligence and computing and cyber science and technology congress, pp 1318–1321

  102. Park JW, Lee H, Chung M (2014) Generation of realistic robot facial expressions for human robot interaction. J Intell Robot Syst 78:443–462

    Article  Google Scholar 

  103. Prajapati S, Shrinivasa Naika CL, Jha S, Nair S (2013) On rendering emotions on a robotic face, pp 1–7

  104. Rabiner LR (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE 77(2):257–286

    Article  Google Scholar 

  105. Ray C, Mondada F, Siegwart R (2008) What do people expect from robots? pp 3816–3821

  106. Ringeval F, Eyben F, Kroupi E, Yuce A, Thiran JP, Ebrahimi T, Lalanne D, Schuller B (2015) Prediction of asynchronous dimensional emotion ratings from audiovisual and physiological data. Pattern Recogn Lett 66:22–30. https://doi.org/10.1016/j.patrec.2014.11.007 (Pattern Recognition in Human Computer Interaction)

    Article  Google Scholar 

  107. Ringeval F, Schuller B, Valstar M, Jaiswal S, Marchi E, Lalanne D, Cowie R, Pantic M (2015) Av+ ec 2015–the first affect recognition challenge bridging across audio, video, and physiological data

  108. Romero P, Cid F, Núnez P (2013) A novel real time facial expression recognition system based on candide-3 reconstruction model. In: Proceedings of the XIV workshop on physical agents (WAF 2013), Madrid, Spain, pp 18–19

  109. Rouast PV, Adam MTP, Chiong R (2019) Deep learning for human affect recognition: insights and new developments. ArXiv arXiv:1901.02884

  110. Ruiz-Garcia A, Elshaw M, Altahhan A, Palade V (2018) A hybrid deep learning neural approach for emotion recognition from facial expressions for socially assistive robots. Neural Comput Appl. https://doi.org/10.1007/s00521-018-3358-8

    Article  Google Scholar 

  111. Saerbeck M, Bartneck C (2010) Perception of affect elicited by robot motion, pp. 53–60

  112. Sariyanidi E, Gunes H, Cavallaro A (2015) Automatic analysis of facial affect: a survey of registration, representation, and recognition. IEEE Trans Pattern Anal Mach Intell 37(6):1113–1133

    Article  Google Scholar 

  113. Saxena S, Tripathi S, Sudarshan TSB (2019) Deep dive into faces: Pose illumination invariant multi-face emotion recognition system. In: 2019 IEEE/RSJ international conference on intelligent robots and systems (IROS), pp. 1088–1093 . https://doi.org/10.1109/IROS40897.2019.8967874

  114. Shi Y, Chen Y, Ardila LR, Venture G, Bourguet ML (2019) A visual sensing platform for robot teachers. In: Proceedings of the 7th international conference on human–agent interaction, pp 200–201

  115. Sikka K, Dhall A, Bartlett M (2015) Exemplar hidden Markov models for classification of facial expressions in videos. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 18–25

  116. Simul NS, Ara NM, Islam MS (2016) A support vector machine approach for real time vision based human robot interaction. In: 2016 19th international conference on computer and information technology (ICCIT), pp 496–500

  117. Srivastava N, Salakhutdinov RR (2012) Multimodal learning with deep Boltzmann machines. In: Pereira F, Burges CJC, Bottou L, Weinberger KQ (eds) Advances in neural information processing systems, vol 25. Curran Associates Inc, Red Hook, pp 2222–2230

    Google Scholar 

  118. Stock R, Merkle M (2018) Can humanoid service robots perform better than service employees? a comparison of innovative behavior cues. https://doi.org/10.24251/HICSS.2018.133

  119. Stock R, Nguyen MA (2019) Robotic psychology what do we know about human–robot interaction and what do we still need to learn?

  120. Stock RM (2016) Emotion transfer from frontline social robots to human customers during service encounters: testing an artificial emotional contagion modell. In: ICIS

  121. Stock RM, Merkle M (2017) A service robot acceptance model: user acceptance of humanoid robots during service encounters. In: IEEE international conference on pervasive computing and communications workshops (PerCom Workshops), pp 339–344 . https://doi.org/10.1109/PERCOMW.2017.7917585

  122. Stock-Homburg R (2021) Survey of emotions in human–robot interaction—after 20 years of research: What do we know and what have we still to learn? Int J Soc Robot

  123. Sukhbaatar S, Makino T, Aihara K, Chikayama T (2011) Robust generation of dynamical patterns in human motion by a deep belief nets. In: Asian conference on machine learning, pp 231–246

  124. Taira H, Haruno M (1999) Feature selection in SVM text categorization. In: AAAI/IAAI, pp 480–486

  125. Tanaka F, Cicourel A, Movellan J (2007) Socialization between toddlers and robots at an early childhood education center. Proc Natl Acad Sci USA 104:17954–8

    Article  Google Scholar 

  126. Uddin MZ, Hassan MM, Almogren A, Alamri A, Alrubaian M, Fortino G (2017) Facial expression recognition utilizing local direction-based robust features and deep belief network. IEEE Access 5:4525–4536

    Article  Google Scholar 

  127. Uddin MZ, Khaksar W, Torresen J (2017) Facial expression recognition using salient features and convolutional neural network. IEEE Access 5:26146–26161

    Article  Google Scholar 

  128. Vapnik VN (1995) The nature of statistical learning theory. Springer, NewYork

    MATH  Book  Google Scholar 

  129. Vapnik VN (1999) An overview of statistical learning theory. IEEE Trans Neural Netw 10(5):988–999

    Article  Google Scholar 

  130. Vithanawasam T, Madhusanka A (2019) Face and upper-body emotion recognition using service robot’s eyes in a domestic environment, pp 44–50

  131. Wang K, Peng X, Yang J, Meng D, Qiao Y (2020) Region attention networks for pose and occlusion robust facial expression recognition. IEEE Trans Image Process 29:4057–4069

    Article  Google Scholar 

  132. Wang Q, Ju S (2008) A mixed classifier based on combination of HMM and KNN. In: 2008 fourth international conference on natural computation, vol 4, pp 38–42

  133. Webb N, Ruiz-Garcia A, Elshaw M, Palade V (2020) Emotion recognition from face images in an unconstrained environment for usage on social robots. In: 2020 international joint conference on neural networks (IJCNN), pp. 1–8

  134. Wimmer M, MacDonald BA, Jayamuni D, Yadav A (2008) Facial expression recognition for human–robot interaction—a prototype. In: Sommer G, Klette R (eds) Robot Vis. Springer, Berlin, pp 139–152

    Chapter  Google Scholar 

  135. Wu C, Wang S, Ji Q (2015) Multi-instance hidden Markov model for facial expression recognition. In: 2015 11th IEEE international conference and workshops on automatic face and gesture recognition (FG), vol 1, pp 1–6

  136. Wu M, Su W, Chen L, Liu Z, Cao W, Hirota K (2019) Weight-adapted convolution neural network for facial expression recognition in human–robot interaction. IEEE Trans Syst Man Cybern Syst

  137. Yaddaden Y, Bouzouane A, Adda M, Bouchard B (2016) A new approach of facial expression recognition for ambient assisted living. In: Proceedings of the 9th ACM international conference on PErvasive technologies related to assistive environments, PETRA ’16. Association for Computing Machinery, New York, NY, USA

  138. Yamashita R, Nishio M, Do RKG, Togashi K (2018) Convolutional neural networks: an overview and application in radiology. Insights Imaging 9(4):611–629

  139. Yang B, Cao J, Ni R, Zhang Y (2018) Facial expression recognition using weighted mixture deep neural network based on double-channel facial images. IEEE Access 6:4630–4640

    Article  Google Scholar 

  140. Yang H, Yin L (2017) CNN based 3d facial expression recognition using masking and landmark features. In: 2017 seventh international conference on affective computing and intelligent interaction (ACII), pp 556–560

  141. Yoo B, Cho S, Kim J (2011) Fuzzy integral-based composite facial expression generation for a robotic head. In: 2011 IEEE international conference on fuzzy systems (FUZZ-IEEE 2011), pp 917–923

  142. Yu C, Tapus A (2019) Interactive robot learning for multimodal emotion recognition. In: Salichs MA, Ge SS, Barakova EI, Cabibihan JJ, Wagner AR, Castro-González Á, He H (eds) Social robotics. Springer, Cham, pp 633–642

    Chapter  Google Scholar 

  143. Zhang F, Zhang T, Mao Q, Xu C (2018) Joint pose and expression modeling for facial expression recognition. In: 2018 IEEE/CVF conference on computer vision and pattern recognition, pp 3359–3368 . https://doi.org/10.1109/CVPR.2018.00354

  144. Zhang F, Zhang T, Mao Q, Xu C (2020) Geometry guided pose-invariant facial expression recognition. IEEE Trans Image Process 29:4445–4460. https://doi.org/10.1109/TIP.2020.2972114

  145. Zhang K, Huang Y, Du Y, Wang L (2017) Facial expression recognition based on deep evolutional spatial-temporal networks. IEEE Trans Image Process 26(9):4193–4203

    MathSciNet  MATH  Article  Google Scholar 

  146. Zhang Z, Luo P, Loy CC, Tang X (2016) From facial expression recognition to interpersonal relation prediction. Int J Comput Vis

  147. Zhao L, Wang Z, Zhang G (2017) Facial expression recognition from video sequences based on spatial-temporal motion local binary pattern and Gabor multiorientation fusion histogram. Math Probl Eng

Download references

Acknowledgements

The authors thank Vignesh Prasad for his insightful comments.

Funding

Open Access funding enabled and organized by Projekt DEAL. This research was funded by the German Research Foundation (DFG, Deutsche Forschungsgemeinschaft). The authors also thank the ZEVEDI Hessen and the leap in time foundation for the grateful funding of the project.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ruth Maria Stock-Homburg.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

The authors declare that there are no compliance issues with this research.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Rawal, N., Stock-Homburg, R.M. Facial Emotion Expressions in Human–Robot Interaction: A Survey. Int J of Soc Robotics (2022). https://doi.org/10.1007/s12369-022-00867-0

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12369-022-00867-0

Keywords

  • Facial emotion recognition
  • Facial emotion expressions
  • Human–robot interaction
  • Survey
  • Overview