The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Face Photo-Sketch Synthesis and Recognition
Abstract
In this paper, we propose a novel face photo-sketch synthesis and recognition method using a multiscale Markov Random Fields (MRF) model. Our system has three components: 1) given a face photo, synthesizing a sketch drawing; 2) given a face sketch drawing, synthesizing a photo; and 3) searching for face photos in the database based on a query sketch drawn by an artist. It has useful applications for both digital entertainment and law enforcement. We assume that faces to be studied are in a frontal pose, with normal lighting and neutral expression, and have no occlusions. To synthesize sketch/photo images, the face region is divided into overlapping patches for learning. The size of the patches decides the scale of local face structures to be learned. From a training set which contains photo-sketch pairs, the joint photo-sketch model is learned at multiple scales using a multiscale MRF model. By transforming a face photo to a sketch (or transforming a sketch to a photo), the difference between photos and sketches is significantly reduced, thus allowing effective matching between the two in face sketch recognition. After the photo-sketch transformation, in principle, most of the proposed face photo recognition approaches can be applied to face sketch recognition in a straightforward way. Extensive experiments are conducted on a face sketch database including 606 faces, which can be downloaded from our Web site (http://mmlab.ie.cuhk.edu.hk/facesketch.html).
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Turk, M., & Pentland, A. (1991) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1991.3.1.71 · Cited by 13,733
Fast approximate energy minimization via graph cuts
Boykov, Y., Veksler, O., & Zabih, R. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.969114 · Cited by 6,988
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Richardson, E., Alaluf, Y., Patashnik, O., Nitzan, Y., Azar, Y., Shapiro, S., & Cohen-Or, D. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00232 · Cited by 1,072
Wang, S., Zhang, L., Liang, Y., & Pan, Q. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6247930 · Cited by 532
A survey on deep learning based face recognition
Guo, G., & Zhang, N. (2019) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2019.102805 · Cited by 485
Coupled information-theoretic encoding for face photo-sketch recognition
Zhang, W., Wang, X., & Tang, X. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995324 · Cited by 403
Self-citationWasserstein CNN: Learning Invariant Features for NIR-VIS Face Recognition
He, R., Wu, X., Sun, Z., & Tan, T. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2842770 · Cited by 317
Metadata
- Authors
- Wang, X., & Tang, X.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2009-11
- Volume
- 31
- Issue
- 11
- Pages
- 1955-1967
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2153288431
- Cited by
- 900
- References
- 2
- FWCI
- 17.18
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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