Emotion Identification and Classification using Convolutional Neural Networks

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


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.

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


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