Simple hybrid classifier for face recognition with adaptively generated virtual data

Ryu, Yeon-Sik; Oh, Se‐Young

doi:10.1016/s0167-8655(01)00159-3

Cited by 41 publications

(16 citation statements)

References 7 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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“…[22][23][24] To this end, various improved methods have been proposed, in which the most simple but effective methods are the virtual sample based methods. [25][26][27][28][29][30] Generally, for face recognition, the more training samples are used, the higher recognition accuracy will be. This is mainly because the face of a person can be better described by more samples.…”

Section: Introductionmentioning

confidence: 99%

“…[20,31,32] For example, considering that single face image is a wide range of uncertainty for representing the face, Xu et al proposed to reduce this uncertainty by viewing the mean of two face images as virtual training samples. [27] Ryu et al [28] exploited the distribution of the virtual training samples which are generated from the given training set. Liu et al also represented every single image by some synthesized (shifted) samples.…”

Section: Introductionmentioning

confidence: 99%

The fusion of original and symmetric virtual images for image preprocessing in face recognition and collaborative representation based classification

Liu

2017

AIR

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Various poses, facial expressions and illuminations are the biggest challenges in the fields of face recognition. To overcome these challenges, we propose a simple yet novel method in this paper by using the approximately symmetrical virtual face. Firstly, based on the symmetrical characteristics of faces, we present the method to generate the virtual faces for all samples in detail. Secondly, the collaborative representation based classification method is performed on both of the original set and virtual set individually. In this way, two kinds of representation residuals of every class can be obtained. Thirdly, an adaptive weighted fusion approach is presented and utilized to integrate those two kinds of representation residuals for face recognition. Lastly, we can obtain the label of the test sample by assigning it to the class with the minimum fused residual. Several experiments are conducted which show that the proposed method not only can greatly improve the classification accuracy, but also can effectively reduce the negative influence of various poses, illuminations, and facial expressions.

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“…al. [12] exploited the distribution of the given training set to generate virtual samples. Beymer et.…”

Section: Introductionmentioning

confidence: 99%

Virtual Samples Construction Using Image-Block-Stretching for Face Recognition

Zhao

Chen

2016

Lecture Notes in Computer Science

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Abstract. Face recognition encounters the problem that multiple samples of the same object may be very different owing to the deformation of appearances. To synthesizing reasonable virtual samples is a good way to solve it. In this paper, we introduce the idea of image-block-stretching to generate virtual images for deformable faces. It allows the neighbored image blocks to be stretching randomly to reflect possible variations of the appearance of faces. We demonstrate that virtual images obtained using image-block-stretching and original images are complementary in representing faces. Extensive classification experiments on face databases show that the proposed virtual image scheme is very competent and can be combined with a number of classifiers, such as the sparse representation classification, to achieve surprising accuracy improvement.

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Simple hybrid classifier for face recognition with adaptively generated virtual data

Cited by 41 publications

References 7 publications

A novel grouped sparse representation for face recognition

A novel grouped sparse representation for face recognition

The fusion of original and symmetric virtual images for image preprocessing in face recognition and collaborative representation based classification

Virtual Samples Construction Using Image-Block-Stretching for Face Recognition

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