Jointly Learning Dictionaries and Subspace Structure for Video-Based Face Recognition

Zhang, Guangxiao; He, Ran; Davis, Larry S.

doi:10.1007/978-3-319-16811-1_7

Cited by 9 publications

(14 citation statements)

References 20 publications

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“…Comparison with State-of-the-art Methods: In this section, we compare our results with several state-of-theart listed next: Discriminant Canonical Correlation analysis (DCC) [20], Manifold-to-Manifold Distance (MMD) [32], Manifold Discriminative Analysis (MDA) [30], Covariance Discriminative Learning (CDL) [31], the linear version of Affine Hull-based Image Set Distance (AHISD) [4], Convex Hull-based Image Set Distance (CHISD) [4] and Sparse Approximated Nearest Points (SANP) [16], Joint Regularized Nearest Points (JRNP) [36], Dictionary-based Face Recognition from Video (DFRV) [8], Joint Dictionary and Subspace Learning (JDSSL) [39]. All the competing methods were implemented using the code provided by the authors except for JDSSL and JRNP.…”

Section: Results and Analysismentioning

confidence: 99%

“…The parameters were tuned based on the settings reported in their papers. We implement the JDSSL following the algorithm in [39] and cite the results directly reported in JRNP [36] as a fair comparison for the Honda/UCSD database 1 .…”

Section: Results and Analysismentioning

confidence: 99%

“…Video-based face recognition has become a very popular topic of research in recent years [8,9,34,15,17,19,30,31,39]. Given a video sequence, the objective is to recognize the person in the video.…”

Section: Introductionmentioning

confidence: 99%

“…This is due to the fact that images could be well represented by an approximately learned dictionary and related sparse codes. However, there are only a few reported efforts on video-based face recognition [8,39,24]. Recently, [8] proposed to partition videos into several clusters and learned a separate sub-dictionary for each cluster.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, information useful for dictionary learning may be lost after projecting all the samples onto the same subspace. [39] learned a sub-dictionary along with a low-rank representation for each subject. However, the sub-dictionaries were independently learned and are not discriminative enough for classification.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Learning a structured dictionary for video-based face recognition

Zheng

Alavi

et al. 2016

2016 IEEE Winter Conference on Applications of Computer Vision (WACV)

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In this paper, we propose a structured dictionary learning framework for video-based face recognition. We discover the invariant structural information from different videos of each subject. Specifically, we employ dictionary learning and low-rank approximation to preserve the invariant structure of face images in videos. The learned dictionary is both discriminative and reconstructive. Thus, we not only minimize the reconstruction error of all the face images but also encourage a sub-dictionary to represent the corresponding subject from different videos. Moreover, by introducing the low-rank approximation, the proposed method is able to discover invariant structured information from different videos of the same subject. To this end, an efficient alternating algorithm is employed to learn our structured dictionary. Extensive experiments on three video-based face recognition databases show that our approach outperforms several state-of-the-art methods.

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Section: Results and Analysismentioning

confidence: 99%

Section: Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Learning a structured dictionary for video-based face recognition

Zheng

Alavi

et al. 2016

2016 IEEE Winter Conference on Applications of Computer Vision (WACV)

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Learning structured ordinal measures for video based face recognition

Tan

Davis

et al. 2018

Pattern Recognition

Self Cite

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This paper presents a structured ordinal measure method for video-based face recognition that simultaneously learns ordinal filters and structured ordinal features. The problem is posed as a non-convex integer program problem that includes two parts. The first part learns stable ordinal filters to project video data into a large-margin ordinal space. The second seeks self-correcting and discrete codes by balancing the projected data and a rank-one ordinal matrix in a structured low-rank way. Unsupervised and supervised structures are considered for the ordinal matrix. In addition, as a complement to hierarchical structures, deep feature representations are integrated into our method to enhance coding stability. An alternating minimization method is employed to handle the discrete and low-rank constraints, yielding high-quality codes that capture prior structures well. Experimental results on three commonly used face video databases show that our method with a simple voting classifier can achieve state-of-the-art recognition rates using fewer features and samples.

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A Band Grouping Based Hyperspectral Imagery Classification Method with Analysis Dictionary Learning

Wang

Zhang

et al. 2018

2018 IEEE Fourth International Conference on Multimedia Big Data (BigMM)

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Jointly Learning Dictionaries and Subspace Structure for Video-Based Face Recognition

Cited by 9 publications

References 20 publications

Learning a structured dictionary for video-based face recognition

Learning a structured dictionary for video-based face recognition

Learning structured ordinal measures for video based face recognition

A Band Grouping Based Hyperspectral Imagery Classification Method with Analysis Dictionary Learning

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