Welcome to Scholar Publishing Group

International Journal of Neural Network, 2022, 3(4); doi: 10.38007/NN.2022.030408.

Deep Convolutional Neural Networks for Facial Expression Recognition

Author(s)

Yan Wang

Corresponding Author:
Yan Wang
Affiliation(s)

Jinzhong University, Jinzhong, China

Abstract

Facial expression recognition technology has been widely used in traffic, medical and criminal investigation and other fields. At present, facial expression recognition technology refers to feature extraction, classification and recognition through facial expression. In order to solve the shortcomings of the existing recognition technology research, this paper discusses the Softmax classifier function equation, facial expression and (DCNN), and briefly discusses the sample data and parameter configuration of the proposed facial expression recognition technology. Finally, the recognition process structure designed in this paper is applied to the five collected facial expressions (angry, sad, happy, afraid and calm) and the other two recognition models (GAN) and (LSTM) to compare the recognition rate. The experimental data show that the recognition rate of the proposed algorithm (CNN) is 95.2% and 94.9%, respectively, which is significantly better than that of the recognition model (GAN) and (LSTM). In the recognition of happiness, fear and calm, the average recognition rate of the proposed (CNN) reaches about 96.3%, while the highest recognition rate of the recognition model (GAN) and (LSTM) is only 92.7%. Therefore, it is verified that facial expression recognition based on depth (CNN) has a good performance effect.

Keywords

Deep Learning, Convolutional Neural Network, Facial Expression, Expression Recognition

Cite This Paper

Yan Wang. Deep Convolutional Neural Networks for Facial Expression Recognition. International Journal of Neural Network (2022), Vol. 3, Issue 4: 61-68. https://doi.org/10.38007/NN.2022.030408.

References

[1] Yolcu G, Oztel I, Kazan S, et al. Facial expression recognition for monitoring neurological disorders based on convolutional neural network. Multimedia Tools and Applications, 2019, 78(22):31581-31603. https://doi.org/10.1007/s11042-019-07959-6

[2] Taee E, Jasim Q M. Blurred Facial Expression Recognition System by Using Convolution Neural Network. Webology, 2020, 17(2):804-816. https://doi.org/10.14704/WEB/V17I2/WEB17068

[3] Chavan U B, Kulkarni D. Optimizing Deep Convolutional Neural Network for Facial Expression Recognition. European Journal of Engineering Research and Science, 2020, 5(2):192-195. https://doi.org/10.24018/ejers.2020.5.2.495

[4] Gogi I, Manhart M, Pandi I S, et al. Fast facial expression recognition using local binary features and shallow neural networks. The Visual Computer, 2020, 36(1):1-16. https://doi.org/10.1007/s00371-018-1585-8

[5] Jain D K, Shamsolmoali P, Sehdev P. Extended Deep Neural Network for Facial Emotion Recognition. Pattern Recognition Letters, 2019, 120(APR.):69-74. https://doi.org/10.1016/j.patrec.2019.01.008

[6] Nour N, Elhebir M, Viriri S. Face Expression Recognition using Convolution Neural Network (CNN) Models. International Journal of Grid Computing & Applications, 2020, 11(4):1-11. https://doi.org/10.5121/ijgca.2020.11401

[7] Aamir M, Ali T, Shaf A, et al. ML-DCNNet: Multi-level Deep Convolutional Neural Network for Facial Expression Recognition and Intensity Estimation. Arabian Journal for Science and Engineering, 2020, 45(12):10605-10620. https://doi.org/10.1007/s13369-020-04811-0

[8] Ahmed M U, Woo K J, Hyeon K Y, et al. Wild Facial Expression Recognition Based on Incremental Active Learning. Cognitive Systems Research, 2018, 52(DEC.):212-222. https://doi.org/10.1016/j.cogsys.2018.06.017

[9] 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

[10] Zarif N E, Montazeri L, Leduc-Primeau F, et al. Mobile-Optimized Facial Expression Recognition Techniques. IEEE Access, 2021, PP(99):1-1. https://doi.org/10.1109/ACCESS.2021.3095844

[11] Mohan K, Seal A, Krejcar O, et al. FER-net: facial expression recognition using deep neural net. Neural Computing and Applications, 2021, 33(15):9125-9136. https://doi.org/10.1007/s00521-020-05676-y

[12] Sajjad M, Zahir S, Ullah A, et al. Human Behavior Understanding in Big Multimedia Data Using CNN based Facial Expression Recognition. Mobile Networks and Applications, 2020, 25(4):1611-1621. https://doi.org/10.1007/s11036-019-01366-9

[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] R, Ramya, K, et al. 3D Facial Expression Recognition Using Multi-channel Deep Learning Framework. Circuits, Systems, and Signal Processing, 2020, 39(2):789-804. https://doi.org/10.1007/s00034-019-01144-8

[15] Fatima, Zahra, Salmam, et al. Fusing multi-stream deep neural networks for facial expression recognition. Signal, Image and Video Processing, 2019, 13(3):609–616. https://doi.org/10.1007/s11760-018-1388-4

[16] Vedantham R. Adaptive increasing-margin adversarial neural iterative system based on facial expression recognition feature models. Multimedia Tools and Applications, 2022, 81(3):3793-3830. https://doi.org/10.1007/s11042-021-11320-1

[17] Khopkar A, Saxena A A. Facial Expression Recognition Using CNN with Keras. Bioscience Biotechnology Research Communications, 2021, 14(5):47-50. https://doi.org/10.21786/bbrc/14.5/10

[18] K`P Dinesh, Jeetha B R. Canny Edge Detection and Contrast Stretching for Facial Expression Detection and Recognition Using Machine Learning. Far East Journal of Electronics and Communications, 2021, 24(1):35-66. https://doi.org/10.17654/EC024010035