Face recognition robust to left/right shadows; facial symmetry

Song, Youngjun; Kim, Young-Gil; Chang, Un-Dong; Kwon, Heak Bong

doi:10.1016/j.patcog.2006.02.018

Cited by 24 publications

(14 citation statements)

References 4 publications

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“…Besides the above-mentioned RBCMs, another way to improve the recognition rate is simultaneously using the original training sample and corresponding virtual samples [16,34,38,40,44,46,47] to recognize the test sample. In real-world application of face recognition, because of the limited training samples, face recognition methods often suffer the challenges of varying poses, illuminations and facial expressions of face image.…”

Section: Introductionmentioning

confidence: 99%

A novel grouped sparse representation for face recognition

Peng

Liu

et al. 2018

Multimed Tools Appl

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Grouped sparse representation classification methods (GSRCMs) have been attracted much attention by scholars, especially in face recognition. However, pervious literatures of GSRCMs only fuse the scores from different groups to classification the test sample, not consider relationships of the groups. Moreover, in real-world application, many methods of face recognition cannot obtain satisfied recognition accuracies because of the variation of poses, illuminations and facial representations of face image. In order to overcome above-mentioned bottlenecks, in this paper, we proposed a novel grouped fusion-based method in face recognition. The proposed method uses the axis-symmetrical property of face to designs a framework and perform it on original training set to generate a kind of virtual samples. The virtual samples are able to reflect the possible change of face images. Meanwhile, to consider the relationship of different groups and strengthen the representation capability of test sample, the proposed method exploits a novel weighted fusion approach to classify the test sample. Experimental results on five face databases demonstrate that our

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Section: Introductionmentioning

confidence: 99%

A novel grouped sparse representation for face recognition

Peng

Liu

et al. 2018

Multimed Tools Appl

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“…For instance in [16], the image enhancement has been considered in face recognition technique. Song et al [17], calculates, prior to feature extraction stage, the illumination difference between right and left part of face. If there is a spacious amount of difference than take the mirror of average illuminated part.…”

Section: Introductionmentioning

confidence: 99%

Image Processing for Face Recognition Rate Enhancement

Abdul-Jabbar¹

2014

IJAST

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“…More recently, Song et al [8] proposed the mirror image method which is based on the assumption of facial symmetry to cope with illumination variation of face image. More specifically, this method is first to divide the original face image into left face image and right face image.…”

Section: Introductionmentioning

confidence: 99%

Illumination-Robust Face Recognition Using an Efficient Mirror Technique

Hsieh

Tung

2009

2009 2nd International Congress on Image and Signal Processing

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In this paper, we propose a novel illumination compensation method for face recognition under variable lighting. This method using an efficient mirror technique can solve the drawbacks of the traditional algorithms such as histogram equalization (HE) and region-based histogram equalization (RHE). Among others, the proposed method has the following advantages: (1) it is very simple so it is easily implemented in a real-time face recognition system; (2) it is able to reinforce key facial features and to standardize other parts of the face; and (3) it can apply directly to single face image without any prior information of light source direction. The experimental results on the Yale B database reveal that the proposed method has rather excellent recognition accuracy.

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Face recognition robust to left/right shadows; facial symmetry

Cited by 24 publications

References 4 publications

A novel grouped sparse representation for face recognition

A novel grouped sparse representation for face recognition

Image Processing for Face Recognition Rate Enhancement

Illumination-Robust Face Recognition Using an Efficient Mirror Technique

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