Graphical Representation for Heterogeneous Face Recognition

Peng, Chunlei; Gao, Xinbo; Wang, Nannan; Li, Jie

doi:10.1109/tpami.2016.2542816

Cited by 117 publications

(60 citation statements)

References 38 publications

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“…Accuracy Method Accuracy Fisherface [41] 28.82% OpenBR [46] 10.80% TFSPS [6] 72.62% MrFSPS [35] 75.36% PLS [17] 51.00% MvDA [19] 55.50% LRBP [24] 91.12% P-RS [3] 83.95% G-HFR [30] 96.04% SGR-DA 96.97%…”

Section: Methodsmentioning

confidence: 99%

“…In existing methods [5], [6], [30], K nearest related neighbors are selected from these related patches and the probe sketch patch y i can be regarded as a linear combination of the K nearest related neighbors weighted by a column vector w yi = (w yi,1 , · · · , w y i,K ) T . A Markov networks model can then be built by jointly modeling all probe sketch patches and their nearest neighbors:…”

Section: A Adaptive Sparse Graphical Representationmentioning

confidence: 99%

“…Because there are 250 pairs in the representation dataset, the size of related neighbors M in our method is 250. To compare with existing methods [5], [6], [30] which manually selected K nearest neighbors, we set the number of nearest neighbors K to be 15,20,25,30,35,40 and the accuracies of different fixed number of related neighbors are shown in Fig. 4, denoted as "Fixed neighbors approach".…”

Section: A Experimental Settingsmentioning

confidence: 99%

“…The forensic sketch database [30] contains 168 real-world forensic sketches with corresponding mug shot photos. Examples of forensic sketch and mug shot photos are shown in Fig.…”

Section: E Forensic Sketch Databasementioning

confidence: 99%

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Sparse graphical representation based discriminant analysis for heterogeneous face recognition

et al. 2019

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Face images captured in heterogeneous environments, e.g., sketches generated by the artists or compositegeneration software, photos taken by common cameras and infrared images captured by corresponding infrared imaging devices, usually subject to large texture (i.e., style) differences. This results in heavily degraded performance of conventional face recognition methods in comparison with the performance on images captured in homogeneous environments. In this paper, we propose a novel sparse graphical representation based discriminant analysis (SGR-DA) approach to address aforementioned face recognition in heterogeneous scenarios. An adaptive sparse graphical representation scheme is designed to represent heterogeneous face images, where a Markov networks model is constructed to generate adaptive sparse vectors. To handle the complex facial structure and further improve the discriminability, a spatial partition-based discriminant analysis framework is presented to refine the adaptive sparse vectors for face matching. We conducted experiments on six commonly used heterogeneous face datasets and experimental results illustrate that our proposed SGR-DA approach achieves superior performance in comparison with state-of-the-art methods.

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Section: Methodsmentioning

confidence: 99%

Section: A Adaptive Sparse Graphical Representationmentioning

confidence: 99%

Section: A Experimental Settingsmentioning

confidence: 99%

“…The forensic sketch database [30] contains 168 real-world forensic sketches with corresponding mug shot photos. Examples of forensic sketch and mug shot photos are shown in Fig.…”

Section: E Forensic Sketch Databasementioning

confidence: 99%

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Sparse graphical representation based discriminant analysis for heterogeneous face recognition

et al. 2019

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“…Exemplar-based face sketch synthesis has received much attention in recent years ranging from digital entertainment to law enforcement [Liu et al, 2007;Wang et al, 2014;Zhang et al, 2015b;Peng et al, 2016b;Peng et al, 2016a;Wang et al, 2017]. Typically these methods usually consist of two steps.…”

Section: Introductionmentioning

confidence: 99%

Fast Preprocessing for Robust Face Sketch Synthesis

Song

Zhang

Bao

et al. 2017

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence

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Exemplar-based face sketch synthesis methods usually meet the challenging problem that input photos are captured in different lighting conditions from training photos. The critical step causing the failure is the search of similar patch candidates for an input photo patch. Conventional illumination invariant patch distances are adopted rather than directly relying on pixel intensity difference, but they will fail when local contrast within a patch changes. In this paper, we propose a fast preprocessing method named Bidirectional Luminance Remapping (BLR), which interactively adjust the lighting of training and input photos. Our method can be directly integrated into state-of-theart exemplar-based methods to improve their robustness with ignorable computational cost 1 .

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