A robust face emotion recognition approach through optimized SIFT features and adaptive deep belief neural network

Abstract

Facial expression is a significant indications for non verbal communication between individuals. The assignment of face emotion recognition is predominantly intricate for two reasons. Initial one is the non-existence of large database of training images and second issue is about classifying the emotions, which can be complex based on if the input image is static or not. Hence, this paper intends to propose a model, where two contributions are made both in the feature extraction and classification process. Initially, in feature extraction process, SIFT features are extracted as it is more associated with facial emotion features. However, numerous key point during SIFT extraction tends to provide redundant information. Hence, the key points are optimized using Whale Optimization Algorithm (WOA). Subsequently, the features extracted from the selected keypoints are multiplied with a weight factor, which has to be optimized by WOA. The resultant features are given to Deep Belief Network (DBN) for the classification of face emotion, in which the number of hidden neurons is also optimized by WOA along with feature weight. It is because of complexity that occurs with the utilization of both forward and reverse training in standard DBN model. Hence, after feature extraction and classification, the face emotion image is recognized accurately, while comparing the proposed Adaptive DBN (ADBN) using WOA over traditional classifiers and other optimization algorithms.