Author:
Barreto Fabian,Sarvaiya Jignesh,Patnaik Suprava
Abstract
For decades, researchers have investigated how to recognize facial images. This study reviews the development of different face recognition (FR) methods, namely, holistic learning, handcrafted local feature learning, shallow learning, and deep learning (DL). With the development of methods, the accuracy of recognizing faces in the labeled faces in the wild (LFW) database has been increased. The accuracy of holistic learning is 60%, that of handcrafted local feature learning increases to 70%, and that of shallow learning is 86%. Finally, DL achieves human-level performance (97% accuracy). This enhanced accuracy is caused by large datasets and graphics processing units (GPUs) with massively parallel processing capabilities. Furthermore, FR challenges and current research studies are discussed to understand future research directions. The results of this study show that presently the database of labeled faces in the wild has reached 99.85% accuracy.
Publisher
Taiwan Association of Engineering and Technology Innovation
Subject
Management of Technology and Innovation,General Engineering,Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Environmental Engineering,General Computer Science
Reference89 articles.
1. G. Guo, et al., “A Survey on Deep Learning Based Face Recognition,” Computer Vision and Image Understanding, vol. 189, Article no. 102805, December 2019.
2. P. Viola, et al., “Rapid Object Detection Using a Boosted Cascade of Simple Features,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1-9, December 2001.
3. M. Wang, et al., “Deep Face Recognition: A Survey,” https://arxiv.org/pdf/1804.06655v2.pdf, April 2018.
4. L. Sirovich, et al., “Low-Dimensional Procedure for the Characterization of Human Faces,” Journal of the Optical Society of America A, vol. 4, pp. 519-524, March 1987.
5. M. Ballantyne, et al., “Woody Bledsoe: His Life and Legacy,” AI Magazine, vol. 17, no. 1, pp. 7-20, 1996.
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