Local graph embedding based on maximum margin criterion via fuzzy set

Wan, Minghua; Lai, Zhihui; Yang, Guowei; Yang, Zai; Zhang, Fanlong; Zheng, Hao

doi:10.1016/j.fss.2016.06.001

Cited by 54 publications

(16 citation statements)

References 15 publications

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“…The proposed S2S distance adopts the kNN-average pooling for the similarity scores computed on all the media in two sets, which makes the identification far less susceptible to the outliers than traditional feature-average pooling. Recently, researchers in [28], [29], [30], [35], and [36] have proposed efficient feature extraction schemes that can be further investigated to develop a face recognition algorithm.…”

Section: Related Workmentioning

confidence: 99%

Boosting the Face Recognition Performance of Ensemble Based LDA for Pose, Non-uniform Illuminations, and Low-Resolution Images

2019

KSII TIIS

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Face recognition systems have several potential applications, such as security and biometric access control. Ongoing research is focused to develop a robust face recognition algorithm that can mimic the human vision system. Face pose, non-uniform illuminations, and lowresolution are main factors that influence the performance of face recognition algorithms. This paper proposes a novel method to handle the aforementioned aspects. Proposed face recognition algorithm initially uses 68 points to locate a face in the input image and later partially uses the PCA to extract mean image. Meanwhile, the AdaBoost and the LDA are used to extract face features. In final stage, classic nearest centre classifier is used for face classification. Proposed method outperforms recent state-of-the-art face recognition algorithms by producing high recognition rate and yields much lower error rate for a very challenging situation, such as when only frontal (0 0) face sample is available in gallery and seven poses (0 0 , ±30 0 , ±35 0 , and ±45 0) as a probe on the LFW and the CMU Multi-PIE databases.

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Section: Related Workmentioning

confidence: 99%

Boosting the Face Recognition Performance of Ensemble Based LDA for Pose, Non-uniform Illuminations, and Low-Resolution Images

2019

KSII TIIS

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“…All of these methods convert image matrix into one highdimension vector [18]- [20]. Yang et al [11], Yang and Yang [12] proposed 2DPCA method that directly uses image matrix, which simplifies the calculation.…”

Section: Introductionmentioning

confidence: 99%

Tri-Direction 2D-Fisher Discriminant Analysis (T2D-FDA) for Feature Extraction

et al. 2019

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A new image feature extraction method for face recognition called Tri-direction 2D-Fisher Discriminant Analysis (T2D-FDA) is proposed to deal with the Small Sample Size (SSS) problem in conventional 1D-Fisher Discriminant Analysis (1D-FDA). Moreover, the essence of T2D-FDA is investigated, and the equivalence of the left-multiplying 2D-FDA of the original image matrices and the left-multiplying D2D-FDA of diagonal image matrices is verified if each column is viewed as a computational unit. Different from the 1D-LDA based approaches, the T2D-FDA is based on 2D image matrices rather than column vectors, so the image matrix does not need to be transformed into a long vector before feature extraction. The advantage arising in this way is that the SSS problem does not exist anymore because within-class scatter matrices constructed is full-rank in T2D-FDA. The proposed method is applied to face recognition where only few training images exist for each subject. Experiment results show T2D-FDA outperforms the current linear subspace methods in SSS problem on four public databases: ORL, Yale, AR and FERET face databases. INDEX TERMS Small sample size (SSS) problem, 2D-fisher discriminant analysis (2D-FDA), face recognition, within-class scatter matrices, linear subspace methods.

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“…These techniques can be generalized under a unified model of linear representation, which reconstruct a query image using a linear combination of available traing samples. Subspace learning based approaches [38] [40] [41] for face recognition is another category of popular and vastly employed techniques among research community (such as PCA, LDA, LPP and LLE etc.). However, methods based on both of these categories required sufficient number of training samples from each class for effective classification, which unfortunality in case of real-world application is extremely difficult to meet.…”

Section: Introductionmentioning

confidence: 99%

“…However, methods based on both of these categories required sufficient number of training samples from each class for effective classification, which unfortunality in case of real-world application is extremely difficult to meet. To address the small sample size problem of face recognition, Wan et al [38] used fuzzy intrinsic and penalty graph to preserve nearest neighbor relationship among images to better characterize the compactness within a class and separate-ability between classes. Then maximum margin criterion (MMC) was used for feature extraction (low-dimensional space) and classification of face images.…”

Section: Introductionmentioning

confidence: 99%

Robust Face Recognition under Limited Training Sample Scenario using Linear Representation

Iqbal¹,

Jadoon²,

Rehman³

et al. 2018

KSII TIIS

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Recently, several studies have shown that linear representation based approaches are very effective and efficient for image classification. One of these linear-representation-based approaches is the Collaborative representation (CR) method. The existing algorithms based on CR have two major problems that degrade their classification performance. First problem arises due to the limited number of available training samples. The large variations, caused by illumintion and expression changes, among query and training samples leads to poor classification performance. Second problem occurs when an image is partially noised (contiguous occlusion), as some part of the given image become corrupt the classification performance also degrades. We aim to extend the collaborative representation framework under limited training samples face recognition problem. Our proposed solution will generate virtual samples and intra-class variations from training data to model the variations effectively between query and training samples. For robust classification, the image patches have been utilized to compute representation to address partial occlusion as it leads to more accurate classification results. The proposed method computes representation based on local regions in the images as opposed to CR, which computes representation based on global solution involving entire images. Furthermore, the proposed solution also integrates the locality structure into CR, using Euclidian distance between the query and training samples. Intuitively, if the query sample can be represented by selecting its nearest neighbours, lie on a same linear subspace then the resulting representation will be more discriminate and accurately classify the query sample. Hence our proposed framework model the limited sample face recognition problem into sufficient training samples problem using virtual samples and intra-class variations, generated from training samples that will result in improved classification accuracy as evident from experimental results. Moreover, it compute representation based on local image patches for robust classification and is expected to greatly increase the classification performance for face recognition task.

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Local graph embedding based on maximum margin criterion via fuzzy set

Cited by 54 publications

References 15 publications

Boosting the Face Recognition Performance of Ensemble Based LDA for Pose, Non-uniform Illuminations, and Low-Resolution Images

Boosting the Face Recognition Performance of Ensemble Based LDA for Pose, Non-uniform Illuminations, and Low-Resolution Images

Tri-Direction 2D-Fisher Discriminant Analysis (T2D-FDA) for Feature Extraction

Robust Face Recognition under Limited Training Sample Scenario using Linear Representation

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