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An efficient CNN approach for facial expression recognition with some measures of overfitting

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Abstract

A person's emotion can be represented through facial expressions in non-vocal communication. Nowadays, automatic facial expression recognition systems have attracted myriad interest in applications such as face biometric-based authentication, behavior analysis (psychology), health monitoring (cerebral palsy), recommendation systems, and many others. Deep learning-based solutions have become the most-handy method to solve any image-video processing problems in recent times. Nevertheless, these CNN models include many hidden layers with complex predefined mathematical functions, resulting in increased complexity. Therefore, deep architecture poses a challenge to deal with a large number of learning parameters. This manuscript proposes two customized-CNN models, named Proposed_Model_1 and Proposed_Model_2, to classify universal facial expressions without overfitting. In this paper, the effect of hyper-parameters such as activation function, learning rate, kernel size, and convolutional block are investigated and optimized efficiently. Experimental results reveal that both of our proposed models outperform other existing methods considering all universal facial expressions, with an accuracy of 67.24% and 66.61%, respectively, on the well-known benchmark public dataset (Facial Expression Recognition-2013).

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References

  1. Borovac B, Gnjatović M, Savić S, Raković M, Nikolić M (2016) Human-like robot marko in the rehabilitation of children with cerebral palsy. New trends in medical and service robots. Springer, pp 191–203

    Chapter  Google Scholar 

  2. Kant Verma K, Singh BM, Dixit A (2019) A review of supervised and unsupervised machine learning techniques for suspicious behavior recognition in intelligent surveillance system. Int J Inf Technol. https://doi.org/10.1007/s41870-019-00364-0

    Article  Google Scholar 

  3. Ansari MA, Singh DK (2021) Monitoring social distancing through human detection for preventing/reducing COVID spread. Int J Inf Technol 13:1255–1264. https://doi.org/10.1007/s41870-021-00658-2

    Article  Google Scholar 

  4. Van Thang, D., Mangla, M., Satpathy, S., Pattnaik, C. R., & Mohanty SN (2021) A fuzzy-based expert system to analyse purchase behaviour under uncertain environment. Int J Inf Technol 13:997–1004. https://doi.org/10.1007/s41870-021-00615-z

    Article  Google Scholar 

  5. Choi IY, Oh MG, Kim JK, Ryu YU (2016) Collaborative filtering with facial expressions for online video recommendation. Int J Inf Manage 36:397–402

    Article  Google Scholar 

  6. Singh DK (2015) Recognizing hand gestures for human computer interaction. In: In 2015 International Conference on Communications and Signal Processing (ICCSP). IEEE, 0379–0382

  7. Ansari MA, Singh DK (2019) An approach for human machine interaction using dynamic hand gesture recognition. In: In 2019 IEEE Conference on Information and Communication Technology. IEEE, 1–6

  8. Rahman A, Mohammed M, Beg S (2018) Face sketch recognition: an application of Z-numbers. Int J Inf Technol 11:541. https://doi.org/10.1007/s41870-018-0178-0

    Article  Google Scholar 

  9. Shah R, Lewis M (2003) Locating the neutral expression in the facial-emotion space. Vis cogn 10:549–566

    Article  Google Scholar 

  10. Sharan P, Sandhya KV, Barya R, Bansal M, Upadhyaya AM (2021) Design and analysis of moems based displacement sensor for detection of muscle activity in human body. Int J Inf Technol 13:397–402

    Google Scholar 

  11. Joormann J, Gotlib IH (2006) Is this happiness I see? Biases in the identification of emotional facial expressions in depression and social phobia. J Abnorm Psychol 115:705

    Article  Google Scholar 

  12. Vijayalakshmi M, Peter VJ (2021) CNN based approach for identifying banana species from fruits. Int J Inf Technol 13:27–32

    Google Scholar 

  13. Gupta M, Calvin MR, Desai B, Bhoir MS (2021) Chest Disease Detection through X-Ray using Machine Learning. Int J Inf Technol 7:46–49

  14. Sharma R, Pachori RB, Sircar P (2020) Automated emotion recognition based on higher order statistics and deep learning algorithm. Biomed Signal Process Control 58:101867

    Article  Google Scholar 

  15. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15:1929–1958

    MathSciNet  MATH  Google Scholar 

  16. Saeed S, Mahmood MK, Khan YD (2018) An exposition of facial expression recognition techniques. Neural Comput Appl 29:425–443

    Article  Google Scholar 

  17. Wan W, Yang C, Li Y (2016) Facial Expression Recognition Using Convolutional Neural Network. A Case Study of the Relationship Between Dataset Characteristics and Network Performance. Stanford University Reports Stanford, Stanford

  18. Liu K, Zhang M, Pan Z (2016) Facial expression recognition with CNN ensemble. In: 2016 international conference on cyberworlds (CW), 163–166

  19. Agrawal A, Mittal N (2020) Using CNN for facial expression recognition: a study of the effects of kernel size and number of filters on accuracy. Vis Comput 36:405–412

    Article  Google Scholar 

  20. Yu Z, Zhang C (2015) Image based static facial expression recognition with multiple deep network learning. In: In Proceedings of the 2015 ACM on international conference on multimodal interaction, 435–442

  21. Van Huynh T, Yang H-J, Lee G-S, Kim S-H, Na I-S (2019) Emotion recognition by integrating eye movement analysis and facial expression model. In: Proceedings of the 3rd International Conference on Machine Learning and Soft Computing. pp 166–169

  22. Chiranjeevi P, Gopalakrishnan V, Moogi P (2015) Neutral face classification using personalized appearance models for fast and robust emotion detection. IEEE Trans Image Process 24:2701–2711

    Article  MathSciNet  Google Scholar 

  23. Perveen N, Guptaand S, Verma K (2012) Facial expression classification using statistical, spatial features and neural network. Int J Adv Eng Technol 4:424

    Google Scholar 

  24. Mollahosseini A, Chan D, Mahoor MH (2016) Going deeper in facial expression recognition using deep neural networks. IEEE Winter Conf Appl Comput Vision. https://doi.org/10.1109/WACV.2016.7477450

    Article  Google Scholar 

  25. Benitez-Quiroz CF, Srinivasan R, Martinez AM (2016) EmotioNet: An accurate, real-time algorithm for the automatic annotation of a million facial expressions in the wild. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. https://doi.org/10.1109/CVPR.2016.600

    Article  Google Scholar 

  26. Li S, Deng W (2019) Reliable crowdsourcing and deep locality-preserving learning for unconstrained facial expression recognition. IEEE Trans Image Process 28:356–370. https://doi.org/10.1109/TIP.2018.2868382

    Article  MathSciNet  MATH  Google Scholar 

  27. Mollahosseini A, Hasani B, Mahoor MH (2019) AffectNet: a database for facial expression, valence, and arousal computing in the wild. IEEE Trans Affect Comput 10:18–31. https://doi.org/10.1109/TAFFC.2017.2740923

    Article  Google Scholar 

  28. Zhang Z, Luo P, Loy CC, Tang X (2018) From facial expression recognition to interpersonal relation prediction. Int J Comput Vis 126:550–569. https://doi.org/10.1007/s11263-017-1055-1

    Article  MathSciNet  Google Scholar 

  29. Cheng S, Kotsia I, Pantic M, Zafeiriou S (2018) 4DFAB: a large scale 4D database for facial expression analysis and biometric applications. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. https://doi.org/10.1109/CVPR.2018.00537

    Article  Google Scholar 

  30. Saeed S, Baber J, Bakhtyar M, Ullah I, Sheikh N, Dad I, Sanjrani AA (2018) Empirical evaluation of svm for facial expression recognition. Int J Adv Comput Sci Appl 9:670–673

    Google Scholar 

  31. Talegaonkar I, Joshi K, Valunj S, Kohok R, Kulkarni A (2019) Real time facial expression recognition using deep learning. In: In Proceedings of International Conference on Communication and Information Processing (ICCIP)

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Authors and Affiliations

  1. Computer Science and Engineering Department, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, Uttar Pradesh, India

    Mayank Kumar Rusia & Dushyant Kumar Singh

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  1. Mayank Kumar Rusia
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  2. Dushyant Kumar Singh
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Correspondence to Mayank Kumar Rusia.

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Rusia, M.K., Singh, D.K. An efficient CNN approach for facial expression recognition with some measures of overfitting. Int. j. inf. tecnol. 13, 2419–2430 (2021). https://doi.org/10.1007/s41870-021-00803-x

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