Based on HOG and DMMA for face recognition with single training sample per person

In order to extract effective features of the complex environment face image, this paper presented a novel method by fusing HOG features and discriminative multi-manifold analysis (DMMA) features. It applied a new adaptive method to calculate similarity between patches of the face image. First,it pa...

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Bibliographic Details
Published inJi suan ji ying yong yan jiu Vol. 32; no. 2; pp. 627 - 634
Main Authors Yang, Xiu-Kun, Yue, Xin-Qi, Ji, Qing-Bo
Format Journal Article
LanguageChinese
Published 01.02.2015
Subjects
Algorithms
Face recognition
Histograms
Samples
Statistical analysis
Statistical methods
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Summary:In order to extract effective features of the complex environment face image, this paper presented a novel method by fusing HOG features and discriminative multi-manifold analysis (DMMA) features. It applied a new adaptive method to calculate similarity between patches of the face image. First,it partitioned each face image into several nonoverlapping patches to form an image set for each sample per person. Then it used histogram of the oriented gradient (HOG) operator to extract image histogram of each an image set. The histogram of each an image set formed a statistics manifold. Last it applied DMMA algorithm to obtain the low-dimensional face image feature. It used the reconstruction-based manifold-manifold distance to identify the unlabeled subjects. Experimental results show that the algorithm for face images of light and geometry changes is superior to the general recognition DMMA algorithms on the AR database and FERET database.
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ISSN:1001-3695
DOI:10.3969/j.issn.1001-3695.2015.02.069