Lighting and Pose Robust Face Sketch Synthesis

Zhang, Wayne; Wang, Xiaogang; Tang, Xiaoou

doi:10.1007/978-3-642-15567-3_31

Cited by 67 publications

(48 citation statements)

References 12 publications

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“…The first category is the synthesis based methods which map data of one modality into another by synthetic methods. Related work includes synthesizing sketches from photos and then comparing synthesized images with sketches drawn by artists [31,36]. One drawback of this kind of methods is that different synthetic methods have to be used if the modalities change.…”

Section: Heterogeneous Face Recognitionmentioning

confidence: 99%

Ensemble of Sparse Cross-Modal Metrics for Heterogeneous Face Recognition

Huo

Shi

Yang

et al. 2016

Proceedings of the 24th ACM International Conference on Multimedia

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Heterogeneous face recognition aims to identify or verify person identity by matching facial images of different modalities. In practice, it is known that its performance is highly influenced by modality inconsistency, appearance occlusions, illumination variations and expressions. In this paper, a new method named as ensemble of sparse cross-modal metrics is proposed for tackling these challenging issues. In particular, a weak sparse cross-modal metric learning method is firstly developed to measure distances between samples of two modalities. It learns to adjust rank-one cross-modal metrics to satisfy two sets of triplet based cross-modal distance constraints in a compact form. Meanwhile, a group based feature selection is performed to enforce that features in the same position of two modalities are selected simultaneously. By neglecting features that attribute to "noise" in the face regions (eye glasses, expressions and so on), the performance of learned weak metrics can be markedly improved. Finally, an ensemble framework is incorporated to combine the results of differently learned sparse metrics into a strong one. Extensive experiments on various face datasets demonstrate the benefit of such feature selection especially when heavy occlusions exist. The proposed ensemble metric learning has been shown superiority over several state-of-the-art methods in heterogeneous face recognition.

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Section: Heterogeneous Face Recognitionmentioning

confidence: 99%

Ensemble of Sparse Cross-Modal Metrics for Heterogeneous Face Recognition

Huo

Shi

Yang

et al. 2016

Proceedings of the 24th ACM International Conference on Multimedia

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show abstract

“…It can be done by either synthesizing sketches from photos or vice-versa. Many works have been proposed in this category including [4,12,13,[24][25][26]30,31,35]. Some of these methods report very good accuracy for sketchphoto recognition on CUFS dataset with appropriate parameters tuned.…”

Section: Related Workmentioning

confidence: 99%

“…Sketch images in the CUFSF are also hand-drawn in the same way as the CUFS dataset so there are now 1,800 sketch-photo pairs in the combined set of CUFS and CUFSF. There are many approaches that have been evaluated on the CUFS dataset including [4,8,9,13,21,[24][25][26][30][31][32]. Recent works have shown close to 100% recognition rate on the CUFS dataset [9,11,32] which implies that the problem is almost solved.…”

Section: Introductionmentioning

confidence: 99%

Data insufficiency in sketch versus photo face recognition

Choi

Sharma

Jacobs

et al. 2012

2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

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Computerized sketch-face recognition is a crucial element for law enforcement and has received considerable attention in the recent literature. Sketches of the suspect are hand-drawn or computer-rendered based on a verbal description of the suspect. However, the most popular and the only publicly available dataset, i.e. the CUFS facesketch dataset, is far from realistic because the sketches are hand-drawn with the artist looking at the photographs to be matched later. After years of effort, researchers are producing nearly perfect results. However, we show that this is not because the problem is solved, but because of flaws in the dataset. In this paper, we empirically show that an off-the-shelf face recognition system for photo-sketch and sketch-photo matching with simple shape and edge features outperforms more sophisticated state-of-the-art approaches even without using training data. We additionally show that just using the hair region gives a 85.22% recognition rate. Based on the empirical evidences we argue that the current dataset available for face-sketch matching purposes is not appropriate and needs to be replaced by a more realistic one for advancement of this field.

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“…These training samples are then used to learn how to generate (or exaggerate) a correct sketch for an unseen input photo. A typical kernel learning method is non-parametric-based Markov random field (MRF) [9,11,12]. For example, work [1] used the E-HMM approach to construct the relation between photo and sketch, where a human face is divided into five parts, namely, forehead, eye, nose, mouth, and chin, in order to preserve detailed textures.…”

Section: Introductionmentioning

confidence: 99%

“…Work [11] used the MRF graph to establish the correlation between the photo and sketch and constrains the smoothness between the neighborhood patches of the synthesized sketch. Work [12] was also based on the MRF approach, but this framework solved the varied light and pose changing conditions by using difference-of-Gaussian (DoG) representations. Work [9] integrated the parametric-based approach in the MRF structure.…”

Section: Introductionmentioning

confidence: 99%

Face Sketch Synthesis System Utilizing Both Global Structure and Local Detailed Texture Components

Chan

2016

Proceedings of the 4th IIAE International Conference on Intelligent Systems and Image Processing 2016

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This study developed a facial sketch synthesis system based on the two-dimensional direct combined model (2DDCM) approach that employs a collection of photo/sketch pairwise training samples. The proposed synthesis framework addresses the following key issues. First, we directly combine each photo and sketch pairwise sample in a concatenated form in order to completely preserve their relationship. Second, photo and sketch images are formed as two-dimensional matrices instead of vectors in order to preserve the facial geometry. Third, both the global facial geometry and the local detailed textures are included in the proposed synthesis framework. Experiments demonstrate our approach can synthesize high quality reconstructed facial sketches from given unseen photos.

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Lighting and Pose Robust Face Sketch Synthesis

Cited by 67 publications

References 12 publications

Ensemble of Sparse Cross-Modal Metrics for Heterogeneous Face Recognition

Ensemble of Sparse Cross-Modal Metrics for Heterogeneous Face Recognition

Data insufficiency in sketch versus photo face recognition

Face Sketch Synthesis System Utilizing Both Global Structure and Local Detailed Texture Components

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