Emotion Identification and Classification using Convolutional Neural Networks

Nishchal Poornadithya C, P.Chimanna Chengappa, Thangaraj Raman, Shantanu Pandey, Gopal Krishna Shyam

Abstract


In this paper we demonstrate the process of emotion detection using convolutional neural network (CNN). Creation of a real-time visual system helps us validate our model. This system achieves the tasks of emotion detection and classification simultaneously in one combined step using the CNN architecture. The training procedural setup is discussed in this paper after which we evaluate specific standard data sets. The evaluation has resulted in accuracies of around 66% in the FER-2013 emotion data set. The implementation of a new real-time guided back propagation technique is also used here. This explains the dynamics of weight changes and evaluates learned features. The gap between slow performances and real-time architectures can be reduced through the careful implementation of modern CNNs, use of ongoing regularization methods and visualization of previously hidden features.

Full Text:

PDF

References


François Chollet. Xception: Deep learning with depthwise separable convolutions. CoRR, abs/1610.02357, 2016. [2] Andrew G. Howard et al. Mobilenets: Efficient convolutional neural networks for mobile vision applications. CoRR, abs/1704.04861, 2017. [3] Dario Amodei et al. Deep speech 2: End-to-end speech recognition in english and mandarin. CoRR, abs/1512.02595, 2015. [4] Ian Goodfellow et al. Challenges in Representation Learning: A report on three machine learning contests, 2013. [5] Xavier Glorot, Antoine Bordes, and Yoshua Bengio. Deep sparse rectifier neural networks. In Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011. [6] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, 2016. [7] Sergey Ioffe and Christian Szegedy. Batch normalization: Accelerating deep network training by

reducing internal covariate shift. In International Conference on Machine Learning, 2015. [8] Diederik Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. [9] Rasmus Rothe, Radu Timofte, and Luc Van Gool. Deep expectation of real and apparent age from a single image without facial landmarks. International Journal of Computer Vision (IJCV), July 2016. [10] Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014. [11] Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller. Striving for simplicity: The all convolutional net. ArXiv preprint arXiv:1412.6806, 2014. [12] Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016. [13] Yichuan Tang. Deep learning using linear support vector machines.arXiv preprint arXiv:1306.0239, 2013




DOI: https://doi.org/10.26483/ijarcs.v9i0.6268

Refbacks

  • There are currently no refbacks.


Bookmark and Share


Copyright (c) 2018 International Journal of Advanced Research in Computer Science