Original and Mirror Face Images and Minimum Squared Error Classification for Visible Light Face Recognition
Keyword(s):
The Face
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In real-world applications, the image of faces varies with illumination, facial expression, and poses. It seems that more training samples are able to reveal possible images of the faces. Though minimum squared error classification (MSEC) is a widely used method, its applications on face recognition usually suffer from the problem of a limited number of training samples. In this paper, we improve MSEC by using the mirror faces as virtual training samples. We obtained the mirror faces generated from original training samples and put these two kinds of samples into a new set. The face recognition experiments show that our method does obtain high accuracy performance in classification.
2016 ◽
Vol 30
(10)
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pp. 1656006
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2016 ◽
Vol 13
(10)
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pp. 7696-7700
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2015 ◽
Vol 63
(6)
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pp. 536-544
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Keyword(s):
2017 ◽
Vol 7
(7)
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pp. 302
Keyword(s):
2014 ◽
Vol 971-973
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pp. 1710-1713
Keyword(s):