Abstract
Facial expressions can convey the internal emotions of a person within a certain scenario and play a major role in the social interaction of human beings. In automatic Facial Expression Recognition (FER) systems, the method applied for feature extraction plays a major role in determining the performance of a system. In this regard, by drawing inspiration from the Swastik symbol, three texture based feature descriptors named Symbol Patterns (SP1, SP2 and SP3) have been proposed for facial feature extraction. SP1 generates one pattern value by comparing eight pixels within a 3\(\times\)3 neighborhood, whereas, SP2 and SP3 generates two pattern values each by comparing twelve and sixteen pixels within a 5\(\times\)5 neighborhood respectively. In this work, the proposed Symbol Patterns (SP) have been evaluated with natural, fibonacci, odd, prime, squares and binary weights for determining the optimal recognition accuracy. The proposed SP methods have been tested on MUG, TFEID, CK+, KDEF, FER2013 and FERG datasets and the results from the experimental analysis demonstrated an improvement in the recognition accuracy when compared to the existing FER methods.
Similar content being viewed by others
Availability of Data and Materials
The datasets used in this work are available in the below links. MUG: https://mug.ee.auth.gr/fed/ TFEID: https://bml.ym.edu.tw/tfeid/modules/wfdownloads/ CK+: https://www.pitt.edu/~emotion/ck-spread.htm KDEF: https://www.kdef.se/download-2/register.html FER2013: https://www.kaggle.com/msambare/fer2013 FERG:http://grail.cs.washington.edu/projects/deepexpr/ferg-2d-db.html
References
Aghamaleki JA, Ashkani Chenarlogh V (2019) Multi-stream cnn for facial expression recognition in limited training data. Multimed Tools Appl 78(16):22861–22882
Aifanti N, Papachristou C, Delopoulos A (2010) The mug facial expression database. In: 11th International Workshop on Image Analysis for Multimedia Interactive Services WIAMIS 10, pages 1–4. IEEE
Aneja D, Colburn A, Faigin G, Shapiro L, Mones B (2016) Modeling stylized character expressions via deep learning. In: Asian conference on computer vision, pages 136–153. Springer
Arshid S, Hussain A, Munir A, Nawaz A, Aziz S (2018) Multi-stage binary patterns for facial expression recognition in real world. Clust Comput 21(1):323–331
Arya R, Vimina ER (2021) Local triangular coded pattern: A texture descriptor for image classification. IETE J Res, pp 1–12
Ashir Abubakar M, Alaa E (2017) Facial expression recognition based on image pyramid and single-branch decision tree. Signal Image Video Process 11(6):1017–1024
Barman A, Dutta P (2019) Influence of shape and texture features on facial expression recognition. IET Image Proc 13(8):1349–1363
Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720
Carrier PL, Courville A, Goodfellow IJ, Mirza M, Bengio Y (2013) Fer-2013 face database. Universit de Montral
Cen S, Yu Y, Yan G, Yu M, Guo Y (2022) Multi-task facial activity patterns learning for micro-expression recognition using joint temporal local cube binary pattern. Signal Process Image Commun, p 116616
Chen L-F, Yen Y-S (2007) Taiwanese facial expression image database. Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan, Brain Mapping Laboratory
Chirra VRR, Uyyala SR, Kolli VKK (2021) Virtual facial expression recognition using deep cnn with ensemble learning. J Ambient Intell Hum Comput, pp 1–19
Dharanya V, Raj ANJ, Gopi Varun P (2021) Facial expression recognition through person-wise regeneration of expressions using auxiliary classifier generative adversarial network (ac-gan) based model. J Vis Commun Image Represent 77:103110
Fan X, Jiang M, Shahid AR, Yan H (2022) Hierarchical scale convolutional neural network for facial expression recognition. Cognit Neurodyn, pp 1–12
Farkhod Makhmudkhujaev Md, Iqbal TB, Ryu B, Chae O (2019) Local directional-structural pattern for person-independent facial expression recognition. Turkish J Electr Eng Comput Sci 27(1):516–531
Farkhod Makhmudkhujaev M, Tauhid A-A-WM, Iqbal B, Ryu B, Chae O (2019) Facial expression recognition with local prominent directional pattern. Signal Process Image Commun 74:1–12
Fussell SR (2002) The verbal communication of emotion: Introduction and overview. In: The verbal communication of emotions, pp 9–24. Psychology Press
Goeleven E, De Raedt R, Leyman L, Verschuere B (2008) The karolinska directed emotional faces: a validation study. Cogn Emot 22(6):1094–1118
Iqbal MTB, Abdullah-Al-Wadud M, Ryu B, Makhmudkhujaev F, Chae O (2018) Facial expression recognition with neighborhood-aware edge directional pattern (nedp). IEEE Trans Affect Comput 11(1):125–137
Kalsum T, Mehmood Z, Kulsoom F, Chaudhry HN, Khan AR, Rashid M, Saba T (2021) Localization and classification of human facial emotions using local intensity order pattern and shape-based texture features. J Intell Fuzzy Syst, (Preprint):1–21
Karnati M, Seal A, Yazidi A, Krejcar O (2021) Fer-net: Facial expression recognition using deep neural net. Neural Comput Appl 33:9125–9136
Kartheek MN, Prasad MV, Bhukya R (2020) Local optimal oriented pattern for person independent facial expression recognition. In: Twelfth International Conference on Machine Vision (ICMV 2019), volume 11433, pages 114330R1–8. International Society for Optics and Photonics
Kartheek MN, Prasad MV, Bhukya R (2021) Radial mesh pattern: a handcrafted feature descriptor for facial expression recognition. J Ambient Intell Human Comput, pp 1–13
Kola DGR, Samayamantula SK (2021) A novel approach for facial expression recognition using local binary pattern with adaptive window. Multimed Tools Appl 80(2):2243–2262
Kola DGR, Samayamantula SK (2021a) Facial expression recognition using singular values and wavelet-based lgc-hd operator. IET Biometrics
Koley S, Roy H, Bhattacharjee D (2021) Gammadion binary pattern of shearlet coefficients (gbpsc): an illumination-invariant heterogeneous face descriptor. Pattern Recogn Lett 145:30–36
Kung H-W, Yi-Han T, Hsu C-T (2015) Dual subspace nonnegative graph embedding for identity-independent expression recognition. IEEE Trans Inf Forensics Secur 10(3):626–639
Lai C-C, Ko C-H (2014) Facial expression recognition based on two-stage features extraction. Optik-Int J Light Electron Opt 125(22):6678–6680
Lucey P, Cohn JF, Kanade T, Saragih J, Ambadar Z, Matthews I (2010) The extended cohn-kanade dataset (ck+): A complete dataset for action unit and emotion-specified expression. In: 2010 Ieee computer society conference on computer vision and pattern recognition-workshops, pages 94–101. IEEE
Majumder A, Behera L, Subramanian VK (2016) Automatic facial expression recognition system using deep network-based data fusion. IEEE Trans Cybern 48(1):103–114
Mandal M, Verma M, Mathur S, Vipparthi SK, Murala S, Kumar DK (2019) Regional adaptive affinitive patterns (radap) with logical operators for facial expression recognition. IET Image Proc 13(5):850–861
Michael RI, Sam EWR (2019) Mdtp: a novel multi-directional triangles pattern for face expression recognition. Multimed Tools Appl 78(18):26223–26238
Min H, Zheng Y, Yang C, Wang X, He L, Ren F (2019) Facial expression recognition using fusion features based on center-symmetric local octonary pattern. IEEE Access 7:29882–29890
Muhamed AA, Hanqi Z, Ibrahim Ali K (2017) An approach for facial expression classification. Int J Biomet 9(2):96–112
Reddy AH, Kolli K, Kiran YL(2021) Deep cross feature adaptive network for facial emotion classification. Signal Image Video Process, pp 1–8
Rivera AR, Castillo JR, Chae O (2015) Local directional texture pattern image descriptor. Pattern Recogn Lett 51:94–100
Rivera AR, Castillo JR, Oksam CO (2012) Local directional number pattern for face analysis: face and expression recognition. IEEE Trans Image Process 22(5):1740–1752
Ryu B, Rivera AR, Kim J, Chae O (2017) Local directional ternary pattern for facial expression recognition. IEEE Trans Image Process 26(12):6006–6018
SL Happy and Aurobinda Routray (2014) Automatic facial expression recognition using features of salient facial patches. IEEE Trans Affect Comput 6(1):1–12
Sen D, Datta S, Balasubramanian R (2019) Facial emotion classification using concatenated geometric and textural features. Multimed Tools Appl 78(8):10287–10323
Shabat AMM, Tapamo J-R (2018) Angled local directional pattern for texture analysis with an application to facial expression recognition. IET Comput Vision 12(5):603–608
Shan C, Gong S, McOwan PW (2009) Facial expression recognition based on local binary patterns: a comprehensive study. Image Vis Comput 27(6):803–816
Shanthi P, Nickolas S (2021) An efficient automatic facial expression recognition using local neighborhood feature fusion. Multimed Tools Appl 80(7):10187–10212
Sun Z, Chiong R, Zheng-ping H (2020) Self-adaptive feature learning based on a priori knowledge for facial expression recognition. Knowl-Based Syst 204:106124
Sun Z, Zheng-ping H, Wang M, Zhao S (2017) Individual-free representation-based classification for facial expression recognition. SIViP 11(4):597–604
Sun Z, Zheng-ping H, Wang M, Zhao S (2019) Dictionary learning feature space via sparse representation classification for facial expression recognition. Artif Intell Rev 51(1):1–18
Swapna A, Bikash S, Prasad MD (2018) Anubhav: recognizing emotions through facial expression. Vis Comput 34(2):177–191
Taskeed Jabid Md, Kabir H, Chae O (2010) Robust facial expression recognition based on local directional pattern. ETRI J 32(5):784–794
Tong Y, Chen R (2019) Local dominant directional symmetrical coding patterns for facial expression recognition. Comput Intell Neurosci 1–13:2019
Tuncer T, Dogan S, Abdar M, Ehsan Basiri M, Plawiak P (2020) A novel facial image recognition method based on perceptual hash using quintet triple binary pattern. Multimed Tools Appl, pages 1–21
Turk M, Pentland A (1991) Face recognition using eigenfaces. In: Proceedings. 1991 IEEE computer society conference on computer vision and pattern recognition, pp 586–587
Uma Maheswari V, Varaprasad G, Viswanadha RS (2021) Local directional maximum edge patterns for facial expression recognition. J Ambient Intell Humaniz Comput 12(5):4775–4783
Verma M, Vipparthi SK, Singh G (2019) Hinet: Hybrid inherited feature learning network for facial expression recognition. IEEE Lett Comput Soc 2(4):36–39
Verma M, Sexena P, Vipparthi S, Singh G (2018) Quest: Quadriletral senary bit pattern for facial expression recognition. In: 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp 1498–1503. IEEE
Verma M, Saxena P, Vipparthi SK, Singh G (2022) Cross-centroid ripple pattern for facial expression recognition. arXiv preprint arXiv:2201.05958
Viola P, Jones MJ (2004) Robust real-time face detection. Int J Comput Vis 57(2):137–154
Wang Y, Li M, Wan X, Zhang C, Wang Y (2020) Multiparameter space decision voting and fusion features for facial expression recognition. Comput Intell Neurosci
Xie S, Haifeng H, Yongbo W (2019) Deep multi-path convolutional neural network joint with salient region attention for facial expression recognition. Pattern Recogn 92:177–191
Yang J, Wang X, Han S, Wang J, Park DS, Wang Y (2019) Improved real-time facial expression recognition based on a novel balanced and symmetric local gradient coding. Sensors 19(8):1899
Yang S, Bhanu B (2011) Facial expression recognition using emotion avatar image. In: Face and Gesture 2011, pp 866–871. IEEE
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Kartheek, M.N., Prasad, M.V.N.K. & Bhukya, R. Texture based feature extraction using symbol patterns for facial expression recognition. Cogn Neurodyn 18, 317–335 (2024). https://doi.org/10.1007/s11571-022-09824-z
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11571-022-09824-z