Expression Recognition Based on Low Pixel Face Images

Fu Liu; Maojun Li; Jianwen Hu; Yuhe Xiao; Zhan Qi

doi:10.3788/LOP57.101008

[1] Jiang M X, Hu M, Wang X H et al. Dual-modal emotion recognition based on facial expression and body posture in video sequences[J]. Laser & Optoelectronics Progress, 55, 071004(2018).

[2] He Z C, Zhao L Z, Chen C. Convolution neural network with multi-resolution feature fusion for facial expression recognition[J]. Laser & Optoelectronics Progress, 55, 071503(2018).

[3] Wang X Y, Fan J Z, Liu H M et al. Multi-expression sequence fusion recognition based on probabilistic cooperative representation[J]. Laser & Optoelectronics Progress, 56, 131004(2019).

[4] Lucey P, Cohn J F, Kanade T et al. The extended cohn-kanade dataset (CK+): a complete dataset for action unit and emotion-specified expression. [C]∥2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops, June 13-18, 2010, San Francisco, CA, USA. New York: IEEE, 94-101(2010).

[5] Li Y, Lin X Z, Jiang M Y. Facial expression recognition with cross-connect LeNet-5 network[J]. Acta Automatica Sinica, 44, 176-182(2018).

[6] Jiang D P, Yang B, Zou L. Facial expression recognition based on local binary mode convolution neural network[J]. Computer Engineering and Design, 39, 1971-1977(2018).

[7] Jain S, Hu C B, Aggarwal J K. Facial expression recognition with temporal modeling of shapes. [C]∥2011 IEEE International Conference on Computer Vision Workshops, November 6-13, 2011, Barcelona, Spain. New York: IEEE, 1642-1649(2011).

[8] King D E. Dlib-ml: a machine learning toolkit[J]. Journal of Machine Learning Research, 10, 1755-1758(2009).

[9] Lopes A T, de Aguiar E, de Souza A F et al. Facial expression recognition with convolutional neural networks: coping with few data and the training sample order[J]. Pattern Recognition, 61, 610-628(2017).

[10] Zhuo D, Jing J F, Zhang H H et al. Classification of chopped strand mat defects based on convolutional neural network[J]. Laser & Optoelectronics Progress, 56, 101009(2019).

[11] Krizhevsky A. Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks. [C]∥Proceedings of the 25th International Conference on Neural Information Processing Systems, December 3-6, 2012, Lake Tahoe, Nevada, USA., 1097-1105(2012).

[12] Szegedy C, Ioffe S, Vanhoucke V. Inception-v4, inception-ResNet and the impact of residual connections on learning. [C]∥ Thirty First AAAI Conference on Artificial Intelligence, February 4-10 , 2017, Hilton, San Francisco Union Square. Palo Alto, California: AAAI, 4278-4284(2017).

[13] Mollahosseini A, Chan D, Mahoor M H. Going deeper in facial expression recognition using deep neural networks. [C]∥2016 IEEE Winter Conference on Applications of Computer Vision, March 7-10, 2016, Lake Placid, NY, USA. New York: IEEE, 1-10(2016).

[14] Simonyan K, Zisserman A[2019-06-15]. Very deep convolutional networks for large-scale image recognition [2019-06-15].https: ∥arxiv., org/abs/1409, 1556.

[15] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition, June 27-30, 2016, Las Vegas, Nevada, USA. New York: IEEE, 770-778(2016).

[16] Srivastava R K, Greff K, Schmidhuber J[2019-06-15]. Training very deep networks [2019-06-15].https:∥arxiv., org/abs/1507, 06228.

[17] Li K. Facial expression recognition based on shallow convolutional neural network[D]. Hefei: University of Science and Technology of China, 42-47(2019).