Shane Cotter
ABSTRACT
The problem of Face Expression Recognition (FER) remains a challenging one due to variations in illumination and pose as well as partial occlusion of the face. Deep neural networks have been increasingly applied to this problem and have achieved excellent recognition results, especially on challenging datasets such as FER2013. However, the trend has been towards more complex networks to increase performance. In this paper, we develop a low complexity model, and we experiment with a variety of parameters to determine the performance of these models on the FER2013 dataset relative to the complexity of the models. We show that we are able to obtain an accuracy of 70.86% on the test FER images which approximately matches the winning entry to the FER2013 competition but our model is 5 times smaller in size. We show that we are able to reduce the model size 5 times more, resulting in a model with fewer than 500,000 parameters, and still maintain an excellent accuracy of 68.43% which would make this model ideal for resource constrained environments.
- "Applications - Keras Documentation." https://keras.io/applications/#available-models.Google Scholar
- A. G. Howard et al., "Mobilenets: Efficient convolutional neural networks for mobile vision applications," ArXiv Prepr. ArXiv170404861, 2017.Google Scholar
- A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," in Advances in neural information processing systems, 2012, pp. 1097--1105.Google Scholar
- C. Darwin, The expression of the emotions in man and animals. London, England: John Murray, 1872.Google Scholar
- C. Pramerdorfer and M. Kampel, "Facial Expression Recognition using Convolutional Neural Networks: State of the Art," ArXiv161202903 Cs, Dec. 2016.Google Scholar
- C. Szegedy et al., "Going deeper with convolutions," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 1--9.Google Scholar
- E. Sariyanidi, H. Gunes, and A. Cavallaro, "Automatic analysis of facial affect: A survey of registration, representation, and recognition," IEEE Trans. Pattern Anal. Mach. Intell., vol. 37, no. 6, pp. 1113--1133, 2015.Google ScholarDigital Library
- F. N. Iandola, S. Han, M. W. Moskewicz, K. Ashraf, W. J. Dally, and K. Keutzer, "SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and < 0.5 MB model size," ArXiv Prepr. ArXiv160207360, 2016.Google Scholar
- I. J. Goodfellow et al., "Challenges in representation learning: A report on three machine learning contests," in International Conference on Neural Information Processing, 2013, pp. 117--124.Google Scholar
- K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770--778.Google Scholar
- K. Simonyan and A. Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition," ArXiv14091556 Cs, Sep. 2014.Google Scholar
- M. Lyons et al., "Coding facial expressions with gabor wavelets," in Proceedings Third IEEE international conference on automatic face and gesture recognition, 1998, pp. 200--205.Google Scholar
- M. Sandler et al., "Mobilenetv2: Inverted residuals and linear bottlenecks," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 4510--4520.Google Scholar
- P. Ekman and W. Friesen, The Facial Action Coding System: A technique for the measurement of facial movement. Palo Alto, CA, USA: Consulting Psychologists Press, 1978.Google Scholar
- P. Lucey et al., "The extended Cohn-Kanade dataset (CK+): A complete dataset for action unit and emotion-specified expression," in 2010 IEEE Conference on Computer Vision and Pattern Recognition, 2010, pp. 94--101.Google Scholar
- W. Liu et al., "A survey of deep neural network architectures and their applications," Neurocomputing, vol. 234, pp. 11--26, 2017.Google Scholar
- Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," Proc. IEEE, vol. 86, no. 11, pp. 2278--2324, 1998.Google ScholarDigital Library
- Y. Tang, "Deep learning using linear support vector machines," ArXiv Prepr. ArXiv13060239, 2013.Google Scholar
Index Terms
- Low Complexity Deep Learning for Mobile Face Expression Recognition
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