Facial expression recognition has always been an interesting and challenging problem, which is easily affected by various factors in practical applications, such as illumination, posture, facial occlusion, age, race and so on. This paper focuses on facial expression recognition based on neural network and feature extraction. In this paper, a new convolutional neural network model for facial expression recognition is proposed to solve the problem that the traditional convolutional neural network is too complex. In the initial stage, the interleaved group convolution structure is adopted, which effectively reduces the number of channel connections between network layers, and at the same time, the interleaved structure also enables effective information communication between different volume units. Through verification and comparison on CK+ and FER datasets in this paper, the improved convolutional neural network facial expression recognition model proposed in this paper can better complete the task of facial expression recognition.

Keyi Yin. Facial Expression Recognition Based on Neural Network and Feature Extraction. International Journal of Neural Network (2020), Vol. 1, Issue 2: 9-16. https://doi.org/10.38007/NN.2020.010202.

[1] Nguyen H D, Yeom S, Lee G S, et al. Facial Emotion Recognition Using an Ensemble of Multi-Level Convolutional Neural Networks. International Journal of Pattern Recognition and Artificial Intelligence, 2018, 33(11):139-149. https://doi.org/10.1142/S0218001419400159

[2] Nishime T, Endo S, Toma N, et al. Feature Acquisition and Analysis for Facial Expression Recognition Using Convolutional Neural Networks. Transactions of the Japanese Society for Artificial Intelligence, 2017, 32(5): F-H34_1-8. https://doi.org/10.1527/tjsai.F-H34

[3] [1]Ruicong, ZHI, Hairui, et al. Combining 3D Convolutional Neural Networks with Transfer Learning by Supervised Pre-Training for Facial Micro-Expression Recognition. IEICE Transactions on Information and Systems, 2019, E102.D (5):1054-1064. https://doi.org/10.1587/transinf.2018EDP7153

[4] Lee J, Kim S, Kim S, et al. Multi-Modal Recurrent Attention Networks for Facial Expression Recognition. IEEE Transactions on Image Processing, 2020, PP(99):1-1. https://doi.org/10.1109/TIP.2020.2996086

[5] Pons G, Masip D. Supervised Committee of Convolutional Neural Networks in Automated Facial Expression Analysis. Affective Computing IEEE Transactions on, 2018, 9(3):343-350. https://doi.org/10.1109/TAFFC.2017.2753235

[6] Suchitra S, Sathya P S, Balachandran P, et al. Intelligent Driver Warning System using Deep Learning-based Facial Expression Recognition. Scopus, 2019, 8(3):831-838. https://doi.org/10.35940/ijrte.C4028.098319

[7] Mohan K, Seal A, Krejcar O, et al. Facial Expression Recognition Using Local Gravitational Force Descriptor-Based Deep Convolution Neural Networks. IEEE Transactions on Instrumentation and Measurement, 2020, PP(99):1-1. https://doi.org/10.1109/TIM.2020.3031835

[8] R, Santhoshkumar, M, et al. Deep Learning Approach for Emotion Recognition from Human Body Movements with Feedforward Deep Convolution Neural Networks - ScienceDirect. Procedia Computer Science, 2019, 152(C):158-165. https://doi.org/10.1016/j.procs.2019.05.038

[9] Alam M, Vidyaratne L S, Iftekharuddin K M. Sparse Simultaneous Recurrent Deep Learning for Robust Facial Expression Recognition. IEEE Transactions on Neural Networks & Learning Systems, 2018, PP(99):1-12. https://doi.org/10.1016/j.neunet.2018.04.020

[10] Jain N, Kumar S, Kumar A , et al. Hybrid deep neural networks for face emotion recognition. Pattern Recognition Letters, 2018, 115(NOV.1):101-106. https://doi.org/10.1016/j.patrec.2018.04.010

[11] Raheem F A, Al A. Deep Learning Convolution Neural Networks Analysis and Comparative Study for Static Alphabet ASL Hand Gesture Recognition. Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University, 2020, 14(4):1871-1881. https://doi.org/10.37896/jxu14.4/212

[12] Renda A, Barsacchi M, Bechini A, et al. Comparing ensemble strategies for deep learning: An application to facial expression recognition. Expert Systems with Application, 2019, 136(Dec.):1-11. https://doi.org/10.1016/j.eswa.2019.06.025

[13] Gupta O, Raviv D, Raskar R. Multi-Velocity Neural Networks for Facial Expression Recognition in Videos. Affective Computing, IEEE Transactions on, 2019, 10(2):290-296. https://doi.org/10.1109/TAFFC.2017.2713355

[14] Rodriguez P, Cucurull G, Gonalez J, et al. Deep Pain: Exploiting Long Short-Term Memory Networks for Facial Expression Classification. IEEE Transactions on Cybernetics, 2017, PP(99):1-11.

[15] Inthiyaz S, Parvez M M, Kumar M S, et al. Facial Expression Recognition Using KERAS. Journal of Physics Conference Series, 2020, 1804(1):012202. https://doi.org/10.1088/1742-6596/1804/1/012202

[16] Mary A H, Kadhim Z B, Sharqi Z S. Face Recognition and Emotion Recognition from Facial Expression Using Deep Learning Neural Network. IOP Conference Series: Materials Science and Engineering, 2020, 928(3):032061 (16pp). https://doi.org/10.1088/1757-899X/928/3/032061

[17] Lee H J, Lee D. Study of Process-Focused Assessment Using an Algorithm for Facial Expression Recognition Based on a Deep Neural Network Model. Electronics, 2020, 10(1):54. https://doi.org/10.3390/electronics10010054

[18] Jayasimha Y, Reddy R. A robust face emotion recognition approach through optimized SIFT features and adaptive deep belief neural network. Intelligent decision technologies, 2019, 13(3):379-390. https://doi.org/10.3233/IDT-190022