Face Recognition Using the SR-CNN Model

doi:10.3390/s18124237

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Face Recognition Using the SR-CNN Model

2018-12-03

DOI：10.3390/s18124237

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Abstract

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In order to solve the problem of face recognition in complex environments being vulnerable to illumination change, object rotation, occlusion, and so on, which leads to the imprecision of target position, a face recognition algorithm with multi-feature fusion is proposed. This study presents a new robust face-matching method named SR-CNN, combining the rotation-invariant texture feature (RITF) vector, the scale-invariant feature trans; m (SIFT) vector, and the convolution neural network (CNN). Furthermore, a graphics processing unit (GPU) is used to parallelize the model; an optimal computational per; mance. The Labeled Faces in the Wild (LFW) database and self-collection face database were selected; experiments. It turns out that the true positive rate is improved by 10.97-13.24% and the acceleration ratio (the ratio between central processing unit (CPU) operation time and GPU time) is 5-6 times; the LFW face database. For the self-collection, the true positive rate increased by 12.65-15.31%, and the acceleration ratio improved by a factor of 6-7.

Keywords:

face matching

scale-invariant feature trans

m (SIFT)

rotation-invariant texture feature (RITF)

convolution neural network (CNN)

graphic processing unit (GPU)

parallel computing

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