Convolutional Neural Networks Architectures for Facial Expression Recognition

This paper presents an overview of some convolutional neural networks architectures and methods for solving the facial expression recognition task using the FER_2013 image dataset. In this sense, three different convolutional networks have been trained and analyzed: a simple sequential architecture, a very lightweight network inspired from the XCEPTION architecture, and the well-known ResNet50 model. Following the experiments conducted using the above-mentioned architectures we have managed to obtain a state-of-the art comparable, 71.25% prediction accuracy on the test subset of FER-2013.

[1]  Tal Hassner,et al.  Effective face frontalization in unconstrained images , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Yichuan Tang,et al.  Deep Learning using Linear Support Vector Machines , 2013, 1306.0239.

[4]  François Chollet,et al.  Xception: Deep Learning with Depthwise Separable Convolutions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Jingying Chen,et al.  Facial expression recognition boosted by soft label with a diverse ensemble , 2019, Pattern Recognit. Lett..

[6]  Emre Akbas,et al.  MicroExpNet: An Extremely Small and Fast Model For Expression Recognition From Frontal Face Images , 2017, ArXiv.

[7]  Emre Akbas,et al.  MicroExpNet: An Extremely Small and Fast Model For Expression Recognition From Face Images , 2017, 2019 Ninth International Conference on Image Processing Theory, Tools and Applications (IPTA).

[8]  Zhenhua Huang,et al.  EdgeCNN: Convolutional Neural Network Classification Model with small inputs for Edge Computing , 2019, ArXiv.

[9]  Yoshua Bengio,et al.  Challenges in representation learning: A report on three machine learning contests , 2013, Neural Networks.

[10]  Xiaoqian Liu,et al.  Improved curriculum learning using SSM for facial expression recognition , 2019, The Visual Computer.

[11]  Luc Van Gool,et al.  Covariance Pooling for Facial Expression Recognition , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[12]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Matias Valdenegro-Toro,et al.  Real-time Convolutional Neural Networks for emotion and gender classification , 2017, ESANN.