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Face Recognition Using the SR-CNN Model
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DOI：10.3390/s18124237.png)
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摘要
En 中文
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.
Keyword:
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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期刊
IF:
3.5
论文数:
7.2W
被引数:
20.9W
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