Open AccessIllumination Invariant Facial Expression Recognition using Convolutional Neural Networks
Author(s) -
K. Prasada Rao*,
M. V. P. Chandra Sekhara Rao
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.d8905.118419
Subject(s) - computer science , convolutional neural network , artificial intelligence , facial expression recognition , facial expression , pattern recognition (psychology) , invariant (physics) , feature extraction , transfer of learning , feature (linguistics) , facial recognition system , deep learning , three dimensional face recognition , speech recognition , machine learning , face detection , mathematics , linguistics , philosophy , mathematical physics
In this work, we propose a prospective novel method to address illumination invariant system for facial expression recognition. Facial expressions are used to convey nonverbal visual information among humans. This also plays a vital role in human-machine interface modules that have invoked attention of many researchers. Earlier machine learning algorithms require complex feature extraction algorithms and are relying on the size and uniqueness of features related to the subjects. In this paper, a deep convolutional neural network is proposed for facial expression recognition and it is trained on two publicly available datasets such as JAFFE and Yale databases under different illumination conditions. Furthermore, transfer learning is used with pre-trained networks such as AlexNet and ResNet-101 trained on ImageNet database. Experimental results show that the designed network could recognize up to 30% variation in the illumination and it achieves an accuracy of 92%.