Semi-Supervised Sparse Representation Based Classification for Face Recognition With Insufficient Labeled Samples

Gao, Yuan; Ma, Jiayi; Yuille, Alan

doi:10.1109/tip.2017.2675341

Cited by 230 publications

(88 citation statements)

References 54 publications

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“…An early finding to compensate visual domain shift in FR systems is to employ a generic set to enrich the diversity of the reference gallery set that is the so-called generic learning concept [14]. Generic learning has been widely discussed by many researchers [15], [16]. Su et al proposed an adaptive generic learning method for FR which utilized external data to estimate the within-class scatter matrix for each individual and applies this information to the reference set [14].…”

Section: B Generic Learningmentioning

confidence: 99%

Domain-Specific Face Synthesis for Video Face Recognition From a Single Sample Per Person

Mokhayeri

Granger

Bilodeau

2019

IEEE Trans.Inform.Forensic Secur.

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In video surveillance, face recognition (FR) systems are employed to detect individuals of interest appearing over a distributed network of cameras. The performance of still-tovideo FR systems can decline significantly because faces captured in unconstrained operational domain (OD) over multiple video cameras have a different underlying data distribution compared to faces captured under controlled conditions in the enrollment domain with a still camera. This is particularly true when individuals are enrolled to the system using a single reference still. To improve the robustness of these systems, it is possible to augment the reference set by generating synthetic faces based on the original still. However, without the knowledge of the OD, many synthetic images must be generated to account for all possible capture conditions. FR systems may, therefore, require complex implementations and yield lower accuracy when training on many less relevant images. This paper introduces an algorithm for domain-specific face synthesis (DSFS) that exploits the representative intra-class variation information available from the OD. Prior to operation (during camera calibration), a compact set of faces from unknown persons appearing in the OD is selected through affinity propagation clustering in the captured condition space (defined by pose and illumination estimation). The domainspecific variations of these face images are then projected onto the reference still of each individual by integrating an imagebased face relighting technique inside the 3-D reconstruction framework. A compact set of synthetic faces is generated that resemble individuals of interest under the capture conditions relevant to the OD. In a particular implementation based on sparse representation classification, the synthetic faces generated with the DSFS are employed to form a cross-domain dictionary that accounts for structured sparsity, where the dictionary blocks combine the original and synthetic faces of each individual. Experimental results obtained with videos from the Chokepoint and COX-S2V data sets reveal that augmenting the reference gallery set of still-to-video FR systems using the proposed DSFS approach can provide a significantly higher level of accuracy compared with the state-of-the-art approaches, with only a moderate increase in its computational complexity.Index Terms-Face recognition, single sample per person, face synthesis, 3D face reconstruction, illumination transferring, sparse representation-based classification, video surveillance.

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Section: B Generic Learningmentioning

confidence: 99%

Domain-Specific Face Synthesis for Video Face Recognition From a Single Sample Per Person

Mokhayeri

Granger

Bilodeau

2019

IEEE Trans.Inform.Forensic Secur.

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“…However, face recognition in low lighting condition is still a very challenging problem, e.g., the near infrared (NIR) image based face recognition [3]. In recent years, VIS-NIR heterogeneous face recognition has attracted more and more attention from researchers and enterprises due to its practical value in many real-world applications, e.g., security surveillance and E-passport [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Facial Feature Embedded Cyclegan For Vis-Nir Translation

Wang

Zhang

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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VIS-NIR face recognition remains a challenging task due to the distinction between spectral components of two modalities and insufficient paired training data. Inspired by the CycleGAN, this paper presents a method aiming to translate VIS face images into fake NIR images whose distributions are intended to approximate those of true NIR images, which is achieved by proposing a new facial feature embedded CycleGAN. Firstly, to learn the particular feature of NIR domain while preserving common facial representation between VIS and NIR domains, we employ a general facial feature extractor (FFE) to replace the encoder in the original generator of CycleGAN. For implementing the facial feature extractor, herein the Mo-bileFaceNet is pretrained on a VIS face database, and is able to extract effective features. Secondly, the domain-invariant feature learning is enhanced by considering a new pixel consistency loss. Lastly, we establish a new WHU VIS-NIR database which varies in face rotation and expressions to enrich the training data. Experimental results on the Oulu-CASIA NIR-VIS database and the WHU VIS-NIR database show that the proposed FFE-based CycleGAN (FFE-CycleGAN) outperforms state-of-the-art VIS-NIR face recognition methods and achieves 96.5% accuracy.

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“…Sparse representation has recently been studied in a variety of problems [28][29][30]. Sparse unmixing is a semi-supervised unmixing method, which assumes that the observed HSI can be formulated to find the optimal subset of pure spectral signatures from a prior large spectral library.…”

Section: Introductionmentioning

confidence: 99%

Sparse Unmixing of Hyperspectral Data with Noise Level Estimation

Mei

et al. 2017

Remote Sensing

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Abstract:Recently, sparse unmixing has received particular attention in the analysis of hyperspectral images (HSIs). However, traditional sparse unmixing ignores the different noise levels in different bands of HSIs, making such methods sensitive to different noise levels. To overcome this problem, the noise levels at different bands are assumed to be different in this paper, and a general sparse unmixing method based on noise level estimation (SU-NLE) under the sparse regression framework is proposed. First, the noise in each band is estimated on the basis of the multiple regression theory in hyperspectral applications, given that neighboring spectral bands are usually highly correlated. Second, the noise weighting matrix can be obtained from the estimated noise. Third, the noise weighting matrix is integrated into the sparse regression unmixing framework, which can alleviate the impact of different noise levels at different bands. Finally, the proposed SU-NLE is solved by the alternative direction method of multipliers. Experiments on synthetic datasets show that the signal-to-reconstruction error of the proposed SU-NLE is considerably higher than those of the corresponding traditional sparse regression unmixing methods without noise level estimation, which demonstrates the efficiency of integrating noise level estimation into the sparse regression unmixing framework. The proposed SU-NLE also shows promising results in real HSIs.Keywords: alternative direction method of multipliers (ADMM); hyperspectral image (HSI); sparse unmixing method based on noise level estimation (SU-NLE)

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Semi-Supervised Sparse Representation Based Classification for Face Recognition With Insufficient Labeled Samples

Cited by 230 publications

References 54 publications

Domain-Specific Face Synthesis for Video Face Recognition From a Single Sample Per Person

Domain-Specific Face Synthesis for Video Face Recognition From a Single Sample Per Person

Facial Feature Embedded Cyclegan For Vis-Nir Translation

Sparse Unmixing of Hyperspectral Data with Noise Level Estimation

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