Robust heterogeneous discriminative analysis for face recognition with single sample per person

Pang, Meng; Cheung, Yiu-ming; Wang, Binghui; Liu, Risheng

doi:10.1016/j.patcog.2019.01.005

Cited by 43 publications

(10 citation statements)

References 43 publications

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“…Proposition 1. Given P = N n=1 P n , the Tucker and CP models, (2) and (3), are equivalent to the PPCA model (1) with the factor matrices W Tucker (1) and…”

Section: B Connections With Existing Ppcasmentioning

confidence: 99%

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Probabilistic Rank-One Tensor Analysis With Concurrent Regularizations

Zhou

Cheung

2021

IEEE Trans. Cybern.

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Subspace learning for tensors attracts increasing interest in recent years, leading to the development of multilinear extensions of Principal Component Analysis (PCA) and Probabilistic PCA (PPCA). Existing multilinear PPCAs are based on the Tucker or CANDECOMP/PARAFAC (CP) models. Although both kinds of multilinear PPCAs have shown their effectiveness in dealing with tensors, they also have their own limitations. Tucker-based multilinear PPCAs have a restrictive subspace representation and suffer from rotational ambiguity, while CPbased ones are more prone to overfitting. To address these problems, we propose Probabilistic Rank-One Tensor Analysis (PROTA), a CP-based multilinear PPCA. PROTA has a more flexible subspace representation than Tucker-based PPCAs, and avoids rotational ambiguity. To alleviate overfitting for CPbased PPCAs, we propose two simple and effective regularization strategies, named as concurrent regularizations. By adjusting the noise variance or the moments of latent features, our strategies concurrently and coherently penalize the whole subspace. This relaxes unnecessary scale restrictions and gains more flexibility in regularizing CP-based PPCAs. To take full advantage of the probabilistic framework, we further propose a Bayesian treatment of PROTA, which achieves both automatic feature determination and robustness against overfitting. Experiments on synthetic and real-world datasets demonstrate the superiority of PROTA in subspace estimation and classification, as well as the effectiveness of concurrent regularizations in alleviating overfitting.

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“…Proposition 1. Given P = N n=1 P n , the Tucker and CP models, (2) and (3), are equivalent to the PPCA model (1) with the factor matrices W Tucker (1) and…”

Section: B Connections With Existing Ppcasmentioning

confidence: 99%

“…tensors, are abundant in real-world applications, such as signal processing, computer vision, social network analysis, etc. [1]- [3]. The order of a tensor is the number of dimensions of the array, and a mode is one dimension of it.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Rank-One Tensor Analysis With Concurrent Regularizations

Zhou

Cheung

2021

IEEE Trans. Cybern.

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“…Zhang et al [12] proposed to learn discriminative features by embedding two sparse graphs into multi-manifold learning. Combining the advantages of [11] and [12], Pang et al [13] proposed to conduct discriminant analysis by introducing a graph-based Fisher-like criterion. Besides the two approaches mentioned here, there is another approach of dealing with the second issue.…”

Section: Related Workmentioning

confidence: 99%

“…They then extract the features by applying LDA, which makes the learned features redundant, because substantial correlation exists among the generated images. And other methods like discriminative multi-manifold analysis (DMMA) [11], sparse discriminative multi-manifold embedding (SDMME) [12], and robust heterogeneous discriminative analysis (RHDA) [13] obtain multiple samples for each subject by dividing each gallery image into a collection of patches. They treat each subject as a manifold and learn multiple discriminative feature spaces.…”

Section: Introductionmentioning

confidence: 99%

Uniform Generic Representation for Single Sample Face Recognition

et al. 2020

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In this paper, we propose a uniform generic representation (UGR) method to solve the single sample per person (SSPP) problem in face recognition, which aims to find consistency between the global and local generic representations. For the local generic representation, we require the probe patches of the same image to be constructed respectively by the corresponding patches of the same gallery image and the intra-class variation dictionaries. Therefore, the probe patches' coefficients, corresponding to patch gallery dictionaries, should be similar to each other. For the global generic representation, the probe image's coefficient, corresponding to the gallery dictionary, should be similar to those of its probe patches. In order to meet the two requirements, we combine local generic representation with global generic representation in soft form. We obtain the representation coefficients by solving a simple quadratic optimization problem. UGR has been evaluated on Extended Yale B, AR, CMU-PIE, and LFW databases. Experimental results show the robustness and effectiveness of our method to illumination, expression, occlusion, time variation, and pose.

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“…For local methods, each face image is first partitioned into a collection of local patches and then some discriminant learning or classification techniques can be applied, such as BlockFLDA [5], DMMA [7], robust heterogeneous discriminative analysis (RHDA) [25], patch based sparse representation for classification (PSRC) [26], patch based collaborative representation for classification (PCRC) [27], and so on.…”

Section: Related Workmentioning

confidence: 99%

Learning Low-Rank Regularized Generic Representation With Block-Sparse Structure for Single Sample Face Recognition

Fan

Ding

et al. 2019

IEEE Access

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In this paper, we propose a novel low-rank regularized generic representation method to address the single sample per person problem in face recognition, which simultaneously employs the structure information from the probe dataset and the generic variation information. Each face image is divided into overlapped patches, whose classification results are aggregated to produce the final result. We generate a subspace for each patch by extracting its eight nearest neighbor patches and explore the relationships between subspaces by imposing a low-rank constraint on the reconstruction coefficients. Moreover, a block-sparsity constraint on the coefficient matrix is imposed to further promote the discrimination of representations. We also propose a dictionary learning method to learn the intra-class facial variations from the generic face datasets, which separates the face contour noise from the variation dictionary by an incoherence regularization item. The experimental results on four public face databases not only show the robustness of our approach to expression, illumination, occlusion, and time variation but also demonstrate the effectiveness of face sketch recognition.

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Robust heterogeneous discriminative analysis for face recognition with single sample per person

Cited by 43 publications

References 43 publications

Probabilistic Rank-One Tensor Analysis With Concurrent Regularizations

Probabilistic Rank-One Tensor Analysis With Concurrent Regularizations

Uniform Generic Representation for Single Sample Face Recognition

Learning Low-Rank Regularized Generic Representation With Block-Sparse Structure for Single Sample Face Recognition

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