Abstract
Character Computing envisions systems that can detect, synthesize, and adapt to human character. The development and realization of this field hinge upon the availability of data about human character traits and states. This data must be comprehensive enough to model the embedded causality in the triad of behavior–situation–character that makes up the core of Character Computing. Acquiring this data requires an intelligent and scalable platform for sensing, processing, analysis, and decision support, which we label as Character-IoT (CIoT). This chapter investigates how this CIoT can be realized. A comprehensive study of sensing modalities in the areas of affective and personality computing is presented to identify the technologies that can be adopted in Character Computing. This includes facial expressions, speech, text, gestures, and others. We also highlight artificial intelligence techniques that are most commonly used in areas of affective and personality computing and analyze which ones are suitable for Character Computing. Finally, we propose an architectural framework for CIoT that can be adopted by future researchers in this field.
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References
Abowd G, Dey A, Brown P, Davies N, Smith M, Steggles P (1999) Towards a better understanding of context and context-awareness. In: Handheld and ubiquitous computing, pp 304–307
Acampora G, Cook DJ, Rashidi P, Vasilakos AV (2013) A survey on ambient intelligence in healthcare. Proc IEEE 101(12):2470–2494
Alarcao SM, Fonseca MJ (2018) Emotions recognition using eeg signals: a survey. IEEE Trans Affect Comput 1–1
Anagnostopoulos C-K, Iliou T, Giannoukos I (2015) Features and classifiers for emotion recognition from speech: a survey from 2000 to 2011. Artif Intell Rev 43(2):155–177
Batrinca L, Mana N, Lepri B, Pianesi F, Sebe N (2011) Please, tell me about yourself: automatic personality assessment using short self-presentations. In: Proceedings of the 13th international conference on multimodal interfaces, ICMI ’11, pp 255–262
Bellavista P, Corradi A, Fanelli M, Foschini L (2012) A survey of context data distribution for mobile ubiquitous systems. ACM Comput Surv 44(4):24:1–24:45
Benkhelifa E, Welsh T, Hamouda W (2018) A critical review of practices and challenges in intrusion detection systems for iot: toward universal and resilient systems. IEEE Commun Surv Tutor 20(4):3496–3509
Biel J, Gatica-Perez D (2013) The youtube lens: crowdsourced personality impressions and audiovisual analysis of vlogs. IEEE Trans Multimed 15(1):41–55
Calvo R, D’Mello S (2010) Affect detection: an interdisciplinary review of models, methods, and their applications. IEEE Trans Affect Comput 1(1):18–37
Cao Z, Simon T, Wei S-E, Sheikh Y (2016) Realtime multi-person 2d pose estimation using part affinity fields. CoRR
Chen L, Hoey J, Nugent CD, Cook DJ, Yu DJ (2012) Sensor-based activity recognition. IEEE Trans Syst Man Cybernet Part C Appl Rev 42(6):790–808
Colombo A, Cusano C, Schettini R (2006) 3d face detection using curvature analysis. J Pattern Recognit 39(3):444–455
Corneanu C, Simon M, Cohn J, Guerrero S (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
Cristani M, Vinciarelli A, Segalin C, Perina A (2013) Unveiling the multimedia unconscious: Implicit cognitive processes and multimedia content analysis. In: Proceedings of the 21st ACM international conference on multimedia, MM ’13
Dhall A, Goecke R, Lucey S, Gedeon T (2011) Acted facial expressions in the wild database. Technical Report, TR-CS-11-02
Einstein G, Kennedy SH, Downar J (2013) Gender/sex differences in emotions. Medicographia 35(3):271–280
Ekman P (1982) Emotion in the human face. Cambridge University Press, Cambridge
Ekman P (1971) Universal and cultural differences in facial expression of emotion. Nebraska Symp Motiv 19:207–283
El-Mougy A, Al-Shiab I, Ibnkahla M (2019) Scalable personalized iot networks. Proc IEEE 107(4):695–710
El-Sayed H, Sankar S, Prasad M, Puthal D, Gupta A, Mohanty M, Lin C (2018) Edge of things: the big picture on the integration of edge, iot and the cloud in a distributed computing environment. IEEE Access 6:1706–1717
Erb B, Meissner D, Kargl F, Steer B, Cuadrado F, Margan D, Pietzuch P (2018) Graphtides: a framework for evaluating stream-based graph processing platforms. In: Proceedings of the 1st ACM SIGMOD joint international workshop on graph data management experiences & systems (GRADES) and network data analytics (NDA), pp 3:1–3:10
Fragopanagos N, Taylor J (2005) Emotion recognition in human-computer interaction. J Neural Netw 18(4):389–405
Giannakopoulos T, Pikrakis A, Theodoridis S (2009) dimensional approach to emotion recognition of speech from movies. In: IEEE international conference on acoustics, speech and signal processing
Golbeck J, Robles C, Edmondson M, Turner K (2011) Predicting personality from twitter. In: 2011 IEEE third international conference on privacy, security, risk and trust and 2011 IEEE third international conference on social computing
Golbeck J, Robles C, Turner K (2011) Predicting personality with social media. In: CHI ’11 extended abstracts on human factors in computing systems, CHI EA ’11
Greene S, Thapliyal H, Caban-Holt A (2016a) A survey of affective computing for stress detection. IEEE Consum Electron Mag 5(4):44–56
Greene S, Thapliyal H, Caban-Holt A (2016b) A survey of affective computing for stress detection: evaluating technologies in stress detection for better health. IEEE Consum Electron Mag 5(4):44–56
Griffin HJ, Aung MS, Romera-Paredes B, McLoughlin C, McKeown G, Curran W, Bianchi-Berthouze N (2013) Laughter type recognition from whole body motion. In: Humane association’s conference on affective computing and intelligent interaction
Jones M, Viola P (2003) Fast multi-view face detection. Mitsubishi Elec. Research Lab, Technical Report, TR2003-96
Kaptein M, Markopoulos P, de Ruyter B, Aarts E (2010) Persuasion in ambient intelligence. J Ambient Intell Hum Comput 1(1):43–56
Kipp M, Martin J-C (2008) Gesture and emotion: can basic gestural form features discriminate emotions? In: IEEE conference on affective computing and intelligent interaction
Kumar VDA, Subramanian M, Gopalakrishnan G, Vengatesan K, Elangovan D, Chitra B (2019) Implementation of the pulse rhythmic rate for the efficient diagnosing of the heartbeat. Healthcare Technol Lett 6(2):48–52
Mairesse F, Walker MA, Mehl M, Moore R (2007) Using linguistic cues for the automatic recognition of personality in conversation and text. J Artif Intell Res 30(1):457–500
Makris P, Skoutas DN, Skianis C (2013) A survey on context-aware mobile and wireless networking: on networking and computing environments’ integration. IEEE Commun Surv Tutor 15(1):362–386
Mao X, Chen L, Fu L (2009) Multi-level speech emotion recognition based on hmm and ann. In: Proceedings of of world congress on computer science and information engineering
McCrae R, Costa P (1996) The five factor model of personality: theoretical perspective. The Guilford Press
Noroozi F, Corneanu C, Kaminska D, Sapinski T, Escalera S, Anbarjafari G (2018) Survey on emotional body gesture recognition. IEEE Trans Affect Comput
Olguin Olguin D,  Gloor P, Pentland A (2009) Capturing individual and group behavior with wearable sensors. In: AAAI spring symposium - Technical Report, pp. 68–74
Papandreou G, Zhu T, Kanazawa N, Toshev A, Tompson J, Bregler C, Murphy K (2017) Towards accurate multi-person pose estimation in the wild. Comput Vis Pattern Recognit 3(4):6
Pease A, Pease B (2004) The Definitive Book of Body Language. In: Peace international
Perera C, Zaslavsky A, Christen P, Georgakopoulos D (2014) Context aware computing for the internet of things: a survey. IEEE Commun Surv Tutor 16(1):414–454
Rashidi P, Cook DJ, Holder LB, Schmitter-Edgecombe M (2011) Discovering activities to recognize and track in a smart environment. IEEE Trans Knowl Data Eng 23(4):527–539
Russel J, Mehrabian A (1977) Evidence for a three-factor theory of emotions. J Res Personal 11:273–294
Senecal S, Cuel L, Aristidou A, Magnenat-Thalmann N (2016) Continuous body emotion recognition system during theater performances. Comput Animat Virtual Worlds 27(3):311–320
Sirohey S (1998) Human face segmentation and identification. Technical Report
Soylemez O, Ergen B, Soylemez N (2017) A 3d facial expression recognition system based on svm classifier using distance based features. In: IEEE conference on signal processing and communications applications (SIU)
Staiano J, Lepri B, Aharony N, Pianesi F, Sebe N, Pentland A (2012) Friends don’t lie: Inferring personality traits from social network structure. In: Proceedings of the 2012 ACM conference on ubiquitous computing, UbiComp ’12, pp 321–330
Steele F, Evans DC, Green RK (2009) Is your profile picture worth 1000 words? photo characteristics associated with personality impression agreement
Tam G, Cheng Z-Q, Lai Y-K, Langbein F, Liu Y, Marshall D, Martin R, Sun X-F, Rosin P (2013) Registration of 3d point clouds and meshes: a survey from rigid to nonrigid. IEEE Trans Vis Comput Graph 19(7):1199–1217
Tomkins S (2008) Affect, Imagery consciousness. Springer Publications, Berlin
Tompson JJ, Jain A, LeCun Y, Bregler C (2014) Joint training of a convolutional network and a graphical model for human pose estimation. In: Advances in neural information processing systems
Trujillo L, Olague G, Hammoud R, Hernandez B (2005) Automatic feature localization in thermal images for facial expression recognition. In: IEEE conference on computer vision and pattern recognition
Vinciarelli A, Mohammadi G (2014) A survey of personality computing. IEEE Trans Affect Comput 5(3):273–291
Vu HA, Yamazaki Y, Dong F, Hirota K (2011) Emotion recognition based on human gesture and speech information using rt middleware. In: IEEE conference on fuzzy systems (FUZZ)
Wang S, He M, Gao Z, He S, Ji Q (2014) Emotion recognition from thermal infrared images using deep boltzmann machine. ACM J Front Comput Sci 8(4):609–618
Wang N, Gao X, Tao D, Yang H, Li X (2018) Facial feature point detection: a comprehensive survey. J Neurocomput 275:50–65
Wang S, Pan B, Chen H, Ji Q (2018) Thermal augmented expression recognition. IEEE Trans Cybern 48(7):2203–2214
Wu C-H, Liang W-B (2011) Emotion recognition of affective speech based on multiple classifiers using acoustic-prosodic information and semantic labels. IEEE Trans Affect Comput 2(1):10–21
Yang C, Ji L, Liu G (2009) Study to speech emotion recognition based on twinssvm. In: Proceedings of 5th international conference on natural computation
Yoshitomi Y, Asada T, Shimada K, Tabuse M (2010) Facial expression recognition for speaker using thermal image processing and speech recognition system. In: International conference on applied computer science
Zeng Z, Pantic M, Roisman G, Huang T (2009) A survey of affect recognition methods: audio, visual, and spontaneous expressions. IEEE Trans Pattern Anal Mach Intell 31(1):39–58
Zen G, Lepri B, Ricci E, Lanz O (2010) Space speaks: Towards socially and personality aware visual surveillance. In: Proceedings of the 1st ACM international workshop on multimodal pervasive video analysis, MPVA ’10, pp 37–42
Zhang CC, Zhang Z (2010) A survey of recent advances in face detection. Microsoft Research, Technical Report, MSR-TR-2010-66
Zhen Q, Huang D, Wang Y, Chen L (2016) Muscular movement model-based automatic 3d/4d facial expression recognition. IEEE Trans Multimed 18(7):1438–1450
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El Mougy, A. (2020). Character-IoT (CIoT): Toward Human-Centered Ubiquitous Computing. In: El Bolock, A., Abdelrahman, Y., Abdennadher, S. (eds) Character Computing. Human–Computer Interaction Series. Springer, Cham. https://doi.org/10.1007/978-3-030-15954-2_7
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