Abstract
This paper investigates feature localization abilities upon injecting noise into the convolutional neural network (CNN). The proposed model intended to classify the 7 human emotional states based on facial expressions and it is shown to perform better than the earlier convolutional neural network. The internal representation of learned features emerges and a more accurate localization of those features appears when independent Gaussian noises are added to certain joints during the deep network training. We observed that the weights after the noise contaminated units lead to output that is more definite. Such behavior improves the network generalization through automatic structuration. We confirmed this by emotion classification experiments on KDEF black and Cohen-Kanade + datasets based on facial expression.
