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Broad Learning System for Tackling Emerging Challenges in Face Recognition

Wenjun Zhang1, Wenfeng Wang2,3,*
1 School of Computer Science and Technology, Hainan University, Haikou, 570228, China
2 Shanghai Institute of Technology, Shanghai, 201418, China
3 Interscience Institute of Management and Technology, Bhubaneswar, 752054, India
* Corresponding Author: Wenfeng Wang. Email: wangwenfeng@sit.edu.cn
(This article belongs to this Special Issue: Enabled and Human-centric Computational Intelligence Solutions for Visual Understanding and Application)

Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2022.020517

Received 28 November 2021; Accepted 15 April 2022; Published online 14 July 2022


Face recognition has been rapidly developed and widely used. However, there is still considerable uncertainty in the computational intelligence based on human-centric visual understanding. Emerging challenges for face recognition are resulted from information loss. This study aims to tackle these challenges with a broad learning system (BLS). We integrated two models, IR3C with BLS and IR3C with a triplet loss, to control the learning process. In our experiments, we used different strategies to generate more challenging datasets and analyzed the competitiveness, sensitivity, and practicability of the proposed two models. In the model of IR3C with BLS, the recognition rates for the four challenging strategies are all 100%. In the model of IR3C with a triplet loss, the recognition rates are 94.61%, 94.61%, 96.95%, 96.23%, respectively. The experiment results indicate that the proposed two models can achieve a good performance in tackling the considered information loss challenges from face recognition.


Computational intelligence; human-centric; visual understanding
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