Abstract
A facial expression is a natural reflection of human feelings, It is the nature of the human to reciprocate through the facial expression to the living world from where the inputs are perceived. The human science measures the emotion, feeling and sentiment by seeing the human face and face curves, but the recognition of emotion through artificial means with high accuracy and less computing resources is more challenging. In this research work, we developed a state-of-the-art procedure that recognizes the emotion of seven categories, namely Happy, Anger, Sad, Disgust, Neutral, Surprise, and Fear efficiently using deep learning. In this work, the model is trained using the fer2013 data set consists of 35887, and the CK48+ dataset consists of 3540 images. We proposed a hybrid model of feature selection that is used before feeding to the proposed computing model of CNN architecture. We claim through the use of both the models one after the other the emotions is correctly recognized with high accuracy during both training and testing phases, which the conventional method doesn’t have.
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References
Mehta D, Siddiqui MFH, Javaid AY (2018) Facial emotion recognition: a survey and real-world user experiences in mixed reality. Sensors 18(2):416
Ekman P (1977) Facial action coding system
Zhang C, Zhang Z (2010) A survey of recent advances in face detection, TechReport, No. MSR-T201066, Microsoft Corporation, Albuquerque, NM, USA
Kumari J, Rajesh R, Pooja K (2015) Facial expression recognition: a survey. Procedia Comput Sci 58:486–491
Muthukrishnan R, Miyilsamy R (2011) Edge detection techniques for image segmentation. Int J Comput Sci Info Technol 3(6):259
James Alex Pappachen (2016) Edge detection for pattern recognition: a survey. Int J Appl Pattern Recogn 3(1):1–21
He K, Xiangyu Z, Shaoqing R, Jian S (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778
Sindagi Vishwanath A, Patel Vishal M (2018) A survey of recent advances in cnn-based single image crowd counting and density estimation. Pattern Recogn Lett 107:3–16
Masakazu M, Katsuhiko M, Yusuke M, Yuji K (2003) Subject independent facial expression recognition with robust face detection using a convolutional neural network. Neural Netw 16(5–6):555–559
Mohammed AA, Rashid M, Wu QMJ, Sid-Ahmed MA (2011) Human face recognition based on multidimensional PCA and extreme learning machine. Pattern Recogn 44(10–11):2588-2597
Rivera AR, Jorge RC, Chae OO (2012) Local directional number pattern for face analysis: face and expression recognition. IEEE Trans Image Process 22(5): 1740–1752
Ebrahimi Kahou S, Michalski V, Konda K, Memisevic R, Pal C (2015) Recurrent neural networks for emotion recognition in video. In:  Proceedings of the 2015 ACM on international conference on multimodal interaction, pp 467–474
Yu Z, Zhang C (2015) Image based static facial expression recognition with multiple deep network learning. In: Proceedings of the 2015 ACM on international conference on multimodal interaction, pp 435–442
Zhang L, Mistry K, Jiang M, Neoh SC, Hossain MA (2015) Adaptive facial point detection and emotion recognition for a humanoid robot. Comput Vis Image Underst 140:93–114
Mollahosseini A, Hassani B, Salvador MJ, Abdollahi H, Chan D, Mahoor MH (2016) Facial expression recognition from world wild web. In: 2016 IEEE Conference on computer vision and pattern recognition workshops (CVPRW) (June 2016) https://doi.org/10.1109/cvprw.2016.188
Guo J, Lei Z, Wan J, Avots E, Hajarolasvadi N, Knyazev B, Anbarjafari G (2018) Dominant and complementary emotion recognition from still images of faces. IEEE Access 6:26391–26403. https://doi.org/10.1109/access.2018.2831927
Le Truc, Duan Ye (2020) REDN: a recursive encoder-decoder network for edge detection. IEEE Access 8:90153–90164. https://doi.org/10.1109/access.2020.2994160
Liu J-J, Hou Q, Cheng M-M (2020) Dynamic feature integration for simultaneous detection of salient object, edge and skeleton. arXiv preprint arXiv:2004.08595(2020)
Liu Y, Xie Z, Liu H (2020) An adaptive and robust edge detection method based on edge proportion statistics. IEEE Trans Image Process 29:5206–5215. https://doi.org/10.1109/tip.2020.2980170
Zhang J, Yu X, Li A, Song P, Liu B, Dai Y (2020) Weakly-supervised salient object detection via scribble annotations. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 12546–12555
Dandan W, Li C, Song H, Xiong H, Liu C, He D (2020) Deep learning approach for apple edge detection to remotely monitor apple growth in orchards. IEEE Access 8:26911–26925. https://doi.org/10.1109/access.2020.2971524
Lo L Xie H-X, Shuai H-H, Cheng W-H (2020) In: MER-GCN: micro expression recognition based on relation modeling with graph convolutional network. arXiv preprint arXiv:2004.08915(2020)
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Srinivas, P.V.V.S., Mishra, P. (2021). Facial Expression Detection Model of Seven Expression Types Using Hybrid Feature Selection and Deep CNN. In: Bhattacharyya, S., Nayak, J., Prakash, K.B., Naik, B., Abraham, A. (eds) International Conference on Intelligent and Smart Computing in Data Analytics. Advances in Intelligent Systems and Computing, vol 1312. Springer, Singapore. https://doi.org/10.1007/978-981-33-6176-8_10
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DOI: https://doi.org/10.1007/978-981-33-6176-8_10
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