Machine Learning
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Nonlinear Approach for Face Sketch Synthesis and Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Separating Style and Content with Bilinear Models
Neural Computation
Face Photo-Sketch Synthesis and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-scale binary patterns for texture analysis
SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
Matching Forensic Sketches to Mug Shot Photos
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bypassing synthesis: PLS for face recognition with pose, low-resolution and sketch
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Coupled information-theoretic encoding for face photo-sketch recognition
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
IEEE Transactions on Circuits and Systems for Video Technology
Face matching between near infrared and visible light images
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Face photo retrieval by sketch example
Proceedings of the 20th ACM international conference on Multimedia
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Automatic face photo-sketch image retrieval has attracted great attention in recent years due to its important applications in real life. The major difficulty in automatic face photo-sketch image retrieval lies in the fact that there exists great discrepancy between the different image modalities (photo and sketch). In order to reduce such discrepancy and improve the performance of automatic face photo-sketch image retrieval, we propose a new framework called multi-feature canonical correlation analysis (MCCA) to effectively address this problem. The MCCA is an extension and improvement of the canonical correlation analysis (CCA) algorithmusing multiple features combined with two different random sampling methods in feature space and sample space. In this framework, we first represent each photo or sketch using a patch-based local feature representation scheme, in which histograms of oriented gradients (HOG) and multi-scale local binary pattern (MLBP) serve as the local descriptors. Canonical correlation analysis (CCA) is then performed on a collection of random subspaces to construct an ensemble of classifiers for photo-sketch image retrieval. Extensive experiments on two public-domain face photo-sketch datasets (CUFS and CUFSF) clearly show that the proposed approach obtains a substantial improvement over the state-of-the-art.