Recognizing faces using Adaptively Weighted Sub-Gabor Array from a single sample image per enrolled subject

Kanan, Hamidreza Rashidy; Faez, Karim

doi:10.1016/j.imavis.2009.06.013

Cited by 30 publications

(14 citation statements)

References 39 publications

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“…The methods being compared are weight local probabilistic approach (wLP) [1], SOM-face method [15] and weighted sub-Gabor array method (WSGA) [6]. They give the performance under each occlusion pattern (glasses or scarfs) separately in each session using only one gallery image, the results are shown in Table 3.From the results we can see that the proposed method consistently achieves much better recognition rates than the other three methods.…”

Section: Methodsmentioning

confidence: 99%

“…In [1], an image-perturbation method is proposed to generate a number of virtual samples in order to overcome the insufficiency of training samples, but the drawback is that the generated samples may be highly correlated and therefore not proper to be used as independent training images. In [6] a local Gabor array is used to represent the sub-patterns of the partitioned face and proposes an adaptively weighting scheme to weight the sub-Gabor features.…”

Section: Introductionmentioning

confidence: 99%

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Robust Face Recognition by Sparse Local Features from a Single Image under Occlusion

Liu

Lai

Qiu

2011

2011 Sixth International Conference on Image and Graphics

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Occlusion and "one sample per person" are two challenging problems for face recognition and still not well solved till now. This paper investigates the two problems and proposes a novel method based on sparse local features to solve them. The contribution of our work is three-fold: first, the key characteristics of successful applying SIFT features for face recognition are analyzed. Second, based on the analysis of SIFT features, two new sparse local feature descriptors, namely Sparse HoG and Sparse LBP are proposed and they are combined together for extracting more discriminative features from an occluded and single image of one person. Third, a new matching strategy is proposed to measure the similarity between the testing and the gallery images. The proposed method is effective and efficient for solving the occlusion and 'one sample per person' problem. Experimental results on the AR database show that the proposed method outperforms the original SIFT, HoG, LBP based methods and also some other existing face recognition algorithms in terms of recognition accuracy.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust Face Recognition by Sparse Local Features from a Single Image under Occlusion

Liu

Lai

Qiu

2011

2011 Sixth International Conference on Image and Graphics

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show abstract

“…This proposed method is evaluated by comparing with some typical relevant algorithms, such as local binary patterns (LBP) [20] feature extraction, local Gabor binary patterns (LGBP) [7] feature extraction, local Gabor (LG) [10] feature extraction, local PCA (LPCA) [30] feature extraction, local ternary patterns (LTP) [31] feature extraction, local comprehensive Gabor histogram (LCGH) and local weighted comprehensive Gabor histogram (LWCGH) feature …”

Section: Comparative Results and Discussionmentioning

confidence: 99%

“…In order to perform realtime recognition, the Gabor wavelets are accelerated on Graphics Processing Units (GPUs) by M. Găianu [6]. On account of extracting much richer texture information, a method based on adaptive weighted sub-Gabor array is presented [10], which contains input variables, complex valued weights, translation parameters and output variables. Generally, the image representations based on Gabor wavelet are widely applied to other pattern recognition fields (e.g., [8,13,14,48]).…”

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confidence: 99%

Face description based on adaptive local weighted Gabor comprehensive histogram feature

Gao

Zhao

Chen

et al. 2016

Multimed Tools Appl

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Face recognition is an extensively research topic in pattern recognition and image processing fields due to its broad application prospects in many areas such as counter terrorism, access identification, electronic passport, e-government affairs, etc. Inspired by the Gabor phase information and local image information content, a novel face description algorithm using adaptive Local Weighted Gabor Comprehensive Histogram (LWGCH) is proposed. It consists of two components: Local Gabor Comprehensive Histogram (LGCH) and Contribution Map (CM). Actually, the adaptive local weighted Gabor comprehensive histogram is generated with LGCH weighed by CM calculated by information content model. Finally, Extensive experiments on ORL, YALE, CMUPIE and Yale B face databases validate the effectiveness of the proposed methods under various conditions of partial occlusion, complex illumination, different expressions and poses. Experimental results indicate that the proposed algorithm is a competitive and robust method compared with several state-of-the-art approaches.

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“…The best of the proposed variations of the algorithm GW+DKPCA get very good results even under varying lighting, expression and perspective conditions. (Kanan & Faez, 2010) presents a new approach for face representation and recognition based on Adaptively Weighted Sub-Gabor Array (AWSGA). The proposed algorithm utilizes a local Gabor array to represent faces partitioned into sub-patterns.…”

Section: State-of-the-art In Single Sample Per Person Face Recognitiomentioning

confidence: 99%

Efficiency of Recognition Methods for Single Sample per Person Based Face Recognition

Oravec¹,

Pavlovičová²,

Mazanec³

et al. 2011

Reviews, Refinements and New Ideas in Face Recognition

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Recognizing faces using Adaptively Weighted Sub-Gabor Array from a single sample image per enrolled subject

Cited by 30 publications

References 39 publications

Robust Face Recognition by Sparse Local Features from a Single Image under Occlusion

Robust Face Recognition by Sparse Local Features from a Single Image under Occlusion

Face description based on adaptive local weighted Gabor comprehensive histogram feature

Efficiency of Recognition Methods for Single Sample per Person Based Face Recognition

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