The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
A Nonlinear Approach for Face Sketch Synthesis and Recognition
Abstract
Most face recognition systems focus on photo-based face recognition. In this paper, we present a face recognition system based on face sketches. The proposed system contains two elements: pseudo-sketch synthesis and sketch recognition. The pseudo-sketch generation method is based on local linear preserving of geometry between photo and sketch images, which is inspired by the idea of locally linear embedding. The nonlinear discriminate analysis is used to recognize the probe sketch from the synthesized pseudo-sketches. Experimental results on over 600 photo-sketch pairs show that the performance of the proposed method is encouraging.
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Citations
Cites
Nonlinear Dimensionality Reduction by Locally Linear Embedding
Roweis, S. T., & Saul, L. K. (2000) · Science · doi:10.1126/science.290.5500.2323 · Cited by 14,965
Turk, M., & Pentland, A. (1991) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1991.3.1.71 · Cited by 13,733
Face recognition: A literature survey
Zhao, W., Chellappa, R., Phillips, P. J., & Rosenfeld, A. (2003) · ACM Computing Surveys · doi:10.1145/954339.954342 · Cited by 6,163
Face recognition by elastic bunch graph matching
Wiskott, L., Fellous, J.-M., Kuiger, N., & von der Malsburg, C. (1997) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.598235 · Cited by 2,883
Fisher discriminant analysis with kernels
Mika, S., Ratsch, G., Weston, J., Scholkopf, B., & Mullers, K. R. (2003) · Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468) · doi:10.1109/nnsp.1999.788121 · Cited by 2,661
Example-based super-resolution
Freeman, W. T., Jones, T. R., & Pasztor, E. C. (2002) · IEEE Computer Graphics and Applications · doi:10.1109/38.988747 · Cited by 2,498
Face Recognition by Elastic Bunch Graph Matching*†
Wiskott, L., Fellous, J.-M., Krüger, N., & von der Malsburg, C. (2022) · INTELLIGENT BIOMETRIC TECHNIQUES in FINGERPRINT and FACE RECOGNITION · doi:10.1201/9780203750520-11 · Cited by 1,781
Face recognition: A literature survey
Arora, U. (2003) · ACM Computing Surveys · Cited by 797
Kernel Eigenfaces vs. Kernel Fisherfaces: Face recognition using kernel methods
Yang, M.-H. (2002) · Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition · doi:10.1109/afgr.2002.4527207 · Cited by 704
Moghaddam, B., Jebara, T., & Pentland, A. (2000) · Pattern Recognition · doi:10.1016/s0031-3203(99)00179-x · Cited by 612
Tang, X., & Wang, X. (2004) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2003.818353 · Cited by 353
Self-citationFace sketch synthesis and recognition
Tang, X., & Wang, X. (2003) · Proceedings Ninth IEEE International Conference on Computer Vision · doi:10.1109/iccv.2003.1238414 · Cited by 281
Self-citationFace photo recognition using sketch
Tang, X., & Wang, X. (2003) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2002.1038008 · Cited by 160
Self-citationFace recognition using Kernel-based Fisher Discriminant Analysis
Liu, Q., Huang, R., Lu, H., & Ma, S. (2003) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/afgr.2002.1004157 · Cited by 134
Self-citationEnhanced Fisher linear discriminant models for face recognition
Liu, C., & Wechsler, H. (2002) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.1998.711956 · Cited by 118
Self-supervised learning for object recognition based on kernel discriminant-EM algorithm
Wu, Y. N., Huang, T. S., & Toyama, K. (2002) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2001.937529 · Cited by 33
Cited by
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
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Metadata
- Authors
- Liu, Q., Tang, X., Jin, H., Lu, H., & Ma, S.
- Venue
- 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)
- Publisher
- IEEE
- Published
- 2005-07-27
- Volume
- 1
- Pages
- 1005-1010
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2141345255
- Cited by
- 357
- References
- 16
- FWCI
- 3.65
- Type
- conference paper
- Language
- en
- Open access
- closed
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