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Convolutional Approach Also Benefits Traditional Face Pattern Recognition Algorithm [208!]

Convolutional Approach Also Benefits Traditional Face Pattern Recognition Algorithm [208!]

Yunke Li, Hongyuan Shi, Liang Chen, Fan Jiang
Copyright: © 2019 |Volume: 11 |Issue: 4 |Pages: 16
ISSN: 1942-9045|EISSN: 1942-9037|EISBN13: 9781522565598|DOI: 10.4018/IJSSCI.2019100101
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MLA

Li, Yunke, et al. "Convolutional Approach Also Benefits Traditional Face Pattern Recognition Algorithm [208!]." IJSSCI vol.11, no.4 2019: pp.1-16. http://doi.org/10.4018/IJSSCI.2019100101

APA

Li, Y., Shi, H., Chen, L., & Jiang, F. (2019). Convolutional Approach Also Benefits Traditional Face Pattern Recognition Algorithm [208!]. International Journal of Software Science and Computational Intelligence (IJSSCI), 11(4), 1-16. http://doi.org/10.4018/IJSSCI.2019100101

Chicago

Li, Yunke, et al. "Convolutional Approach Also Benefits Traditional Face Pattern Recognition Algorithm [208!]," International Journal of Software Science and Computational Intelligence (IJSSCI) 11, no.4: 1-16. http://doi.org/10.4018/IJSSCI.2019100101

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Abstract

Convolutional neural networks (CNN) are widely used deep learning frameworks and are applied in the field of face recognition, achieving outstanding results. The Macropixel comparison approach is a shallow mathematical approach that recognizes faces by comparing the original pixel blocks of face images. In this article, the authors are inspired by ideas of the currently popular deep neural network framework and introduce two features into the mathematical approach: deep overlap and weighted filter. The aim is to explore if the idea of deep learning could benefit mathematical recognition method, which might extend the scope of face recognition research. Results from the experiments show that the new proposed approach achieves markedly better recognition rates than the original macropixel methods.

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