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

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

Yunke Li (University of Northern British Columbia, Prince George, Canada), Hongyuan Shi (University of Northern British Columbia, Prince George, Canada), Liang Chen (University of Northern British Columbia, Prince George, Canada) and Fan Jiang (University of Northern British Columbia, Prince George, Canada)
DOI: 10.4018/IJSSCI.2019100101

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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Convolutional Macropixel Comparison Approach

In the research of CNN, convolutional layer has been found to be extremely effective in extracting features from face images. Inspired by structure of the layer, two key concepts are introduced into the original approach of macropixel: heavily over lapped comparison and weighted filter counter. Both of them serve the purpose of obtaining richer feature information from macropixels. The whole framework is shown in Figure 1.

Figure 1.

The system is divided into two stages: Training stage and recognition stage. The training stage produces weighted filter which is used on original recognition. One thing to note here is that all operations are employing heavy overlap.

IJSSCI.2019100101.f01

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