Open Access

ARTICLE

Automated Facial Expression Recognition and Age Estimation Using Deep Learning

by Syeda Amna Rizwan¹, Yazeed Yasin Ghadi², Ahmad Jalal¹, Kibum Kim^3,*

1 Department of Computer Science, Air University, Islamabad, 44000, Pakistan
2 Department of Computer Science and Software Engineering, Al Ain University, Abu Dhabi, 122612, UAE
3 Department of Human-Computer Interaction, Hanyang University, Ansan, 15588, Korea

* Corresponding Author: Kibum Kim. Email:

Computers, Materials & Continua 2022, 71(3), 5235-5252. https://doi.org/10.32604/cmc.2022.023328

Received 03 September 2021; Accepted 08 November 2021; Issue published 14 January 2022

Abstract

With the advancement of computer vision techniques in surveillance systems, the need for more proficient, intelligent, and sustainable facial expressions and age recognition is necessary. The main purpose of this study is to develop accurate facial expressions and an age recognition system that is capable of error-free recognition of human expression and age in both indoor and outdoor environments. The proposed system first takes an input image pre-process it and then detects faces in the entire image. After that landmarks localization helps in the formation of synthetic face mask prediction. A novel set of features are extracted and passed to a classifier for the accurate classification of expressions and age group. The proposed system is tested over two benchmark datasets, namely, the Gallagher collection person dataset and the Images of Groups dataset. The system achieved remarkable results over these benchmark datasets about recognition accuracy and computational time. The proposed system would also be applicable in different consumer application domains such as online business negotiations, consumer behavior analysis, E-learning environments, and emotion robotics.

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Keywords

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