Local Difference of Gaussian Binary Pattern: Robust Features for Face Sketch Recognition

Alex, Ann Theja; Asari, Vijayan K.; Mathew, Alex

doi:10.1109/smc.2013.210

Cited by 19 publications

(13 citation statements)

References 16 publications

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“…However, since the projection procedure reduces the discriminability, it degrades the performance of HFR methods. The modality invariant feature descriptor based methods [1], [3], [21], [22], [23], [24], [25], [26], [27], [28], [29] first represent face images by extracting modality invariant features which are then measured for matching. Yet most of these methods extract feature descriptors ignoring the facial spatial information and thus these methods have limited discriminability.…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

Sparse graphical representation based discriminant analysis for heterogeneous face recognition

et al. 2019

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“…The comparison was made between CCA Fusion (our baseline) and CCA Fusion with D-DoGoGH on PoI matching methods. This is because the CCA Fusion matching method has outperformed the state-of-the-art methods (e.g., MCCA [68], CITE [29], LRBP [17], LDoGBP [69], and C-DoGOGH [44]) and thus be the baseline for the improved matching method (i.e., CCA Fusion with D-DoGoGH on PoI). From Table 2, the results demonstrate that the accuracy improvement for viewed sketch is not significant in comparison with the semi forensic and forensic sketch.…”

Section: B Resultsmentioning

confidence: 99%

Canonical Correlation Analysis Feature Fusion With Patch of Interest: A Dynamic Local Feature Matching for Face Sketch Image Retrieval

2020

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An automatic photo retrieval system based on a face sketch has very useful application as to narrow down potential suspects in criminal investigations. This is true when there is no other evident except the face sketch that is rendered based on the recollection of a victim or eyewitness. Among the noticeable difficulties in matching the sketch and photo due to its modality difference are the generated sketch has some tendency of shape exaggeration, the sketch has very less accurate details and the real-world photo may expose to lighting variation unlike the sketch. In this paper, we attempt to address these complications by matching the sketch and photos using dynamic local feature of Difference of Gaussian Oriented Gradient Histogram (DoGOGH) on some selected patches. To avoid discriminative power degradation due to a large number of gallery images, two stage matching blocks are introduced in a cascaded fashion. The front block matches the feature such that it short lists k most similar photos for the second block. In this front block, Histogram of Oriented Gradient (HOG) and Gabor Wavelet (GW) features are fused by maximizing the correlation between the two using Canonical Correlation Analysis (CCA). Based on the short listed photos, the following block re-matched the sketch and photos using dynamically extracted local feature on its Patch of Interest (PoI). Eventually, the matching scores from the blocks are fused before getting rank-1 accuracy. The experimental results on two baseline datasets indicate that the proposed method outperforms the stateof-the-art methods. The extended evaluation on semi-forensic and forensic sketch datasets demonstrate its usage feasibility.

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“…Accuracy Method Accuracy TFSPS [4] 72.62% PLS [11] 51% MvDA [7] 55.50% LRBP [13] 91.12% LDoGBP [29] 91.04% G-HFR 96.04%…”

Section: Methodsmentioning

confidence: 99%

Graphical Representation for Heterogeneous Face Recognition

Peng

Gao

Wang

et al. 2017

IEEE Trans. Pattern Anal. Mach. Intell.

120

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Abstract-Heterogeneous face recognition (HFR) refers to matching face images acquired from different sources (i.e., different sensors or different wavelengths) for identification. HFR plays an important role in both biometrics research and industry. In spite of promising progresses achieved in recent years, HFR is still a challenging problem due to the difficulty to represent two heterogeneous images in a homogeneous manner. Existing HFR methods either represent an image ignoring the spatial information, or rely on a transformation procedure which complicates the recognition task. Considering these problems, we propose a novel graphical representation based HFR method (G-HFR) in this paper. Markov networks are employed to represent heterogeneous image patches separately, which takes the spatial compatibility between neighboring image patches into consideration. A coupled representation similarity metric (CRSM) is designed to measure the similarity between obtained graphical representations. Extensive experiments conducted on multiple HFR scenarios (viewed sketch, forensic sketch, near infrared image, and thermal infrared image) show that the proposed method outperforms state-of-the-art methods.

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Local Difference of Gaussian Binary Pattern: Robust Features for Face Sketch Recognition

Cited by 19 publications

References 16 publications

Sparse graphical representation based discriminant analysis for heterogeneous face recognition

Sparse graphical representation based discriminant analysis for heterogeneous face recognition

Canonical Correlation Analysis Feature Fusion With Patch of Interest: A Dynamic Local Feature Matching for Face Sketch Image Retrieval

Graphical Representation for Heterogeneous Face Recognition

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