The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Coupled information-theoretic encoding for face photo-sketch recognition
Abstract
Automatic face photo-sketch recognition has important applications for law enforcement. Recent research has focused on transforming photos and sketches into the same modality for matching or developing advanced classification algorithms to reduce the modality gap between features extracted from photos and sketches. In this paper, we propose a new inter-modality face recognition approach by reducing the modality gap at the feature extraction stage. A new face descriptor based on coupled information-theoretic encoding is used to capture discriminative local face structures and to effectively match photos and sketches. Guided by maximizing the mutual information between photos and sketches in the quantized feature spaces, the coupled encoding is achieved by the proposed coupled information-theoretic projection tree, which is extended to the randomized forest to further boost the performance. We create the largest face sketch database including sketches of 1, 194 people from the FERET database. Experiments on this large scale dataset show that our approach significantly outperforms the state-of-the-art methods.
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Cites
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Cited by
Face Alignment at 3000 FPS via Regressing Local Binary Features
Ren, S., Cao, X., Wei, Y., & Sun, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.218 · Cited by 853
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
Metadata
- Authors
- Zhang, W., Wang, X., & Tang, X.
- Venue
- CVPR 2011
- Publisher
- IEEE
- Published
- 2011-06
- Pages
- 513-520
- OpenAlex
- W2034136097
- Cited by
- 403
- References
- 35
- FWCI
- 12.91
- Type
- conference paper
- Language
- en
- Open access
- closed
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