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Robust Facial Expression Recognition based on Convolutional Neural Network in Pose and Occlusion

Shalmiya P.*, G. Thirugnanam **

*-** Department of Electronics and Communication Engineering, Government College of Technology, Coimbatore, Tamil Nadu, India.

Periodicity:July - December'2020
DOI : https://doi.org/10.26634/jpr.7.2.18083

Abstract

Facial expression plays an important role in human-human communication. Facial Expression Recognition (FER) has various applications in attendance management system, service robots, intelligent tutoring system, driver fatigue monitoring, human behavior understanding, detection of mental disorders and has various medical and surveillance applications. The various limitations of FER are occlusion, illumination variation, variant pose, poor image quality, etc. This work mainly focuses on occluded and pose variations datasets. These issues have included wide interest in FER where it leads to unseen regions of input faces which brings difficulty for face identification and face recognition and also affects the feature extraction and classification process. Thus, these effects can be reduced and the expressions are recognised using Convolutional Neural Network (CNN) architecture. Finally, the performance metrics of the model has been measured with accuracy values of the system.

Keywords

Facial Expression Recognition, Convolutional Neural Network, Occlusion, Pose Invariant.

How to Cite this Article?

Shalmiya, P., and Thirugnanam, G. (2020). Robust Facial Expression Recognition based on Convolutional Neural Network in Pose and Occlusion. i-manager's Journal on Pattern Recognition, 7(2), 14-22. https://doi.org/10.26634/jpr.7.2.18083

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