Illumination Invariant Facial Expression Recognition using Convolutional Neural Networks
K. Prasada Rao1, M. V. P. Chandra Sekhara Rao2
1K. Prasada Rao , pursuing Ph.D in Computer Science and Engineering from Acharya Nagarjuna University, Guntur, AP, India.
2Dr. M. V. P. Chandra Sekhara Rao, professor at Dept of CSE, RVR&JC College of Engineering, Guntur, AP, India.
Manuscript received on November 12, 2019. | Revised Manuscript received on November 25, 2019. | Manuscript published on 30 November, 2019. | PP: 6140-6164 | Volume-8 Issue-4, November 2019. | Retrieval Number: D8905118419/2019©BEIESP | DOI: 10.35940/ijrte.D8905.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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%.
Keywords: Classification, Convolutional Neural Network, Facial Expression Recognition, Illumination.
Scope of the Article: Classification.