IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using the Discrete Cosine Transform
International Journal of Computer Vision - Special issue: Research at McGill University
Overview of the Face Recognition Grand Challenge
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Component-based LDA face description for image retrieval and MPEG-7 standardisation
Image and Vision Computing
Enhanced local texture feature sets for face recognition under difficult lighting conditions
IEEE Transactions on Image Processing
Learning the Uncorrelated, Independent, and Discriminating Color Spaces for Face Recognition
IEEE Transactions on Information Forensics and Security
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Robust coding schemes for indexing and retrieval from large face databases
IEEE Transactions on Image Processing
A Comparative Study of Local Matching Approach for Face Recognition
IEEE Transactions on Image Processing
Color Image Discriminant Models and Algorithms for Face Recognition
IEEE Transactions on Neural Networks
A mutual information based face clustering algorithm for movie content analysis
Image and Vision Computing
Face recognition with directional local binary patterns
CCBR'11 Proceedings of the 6th Chinese conference on Biometric recognition
Facial expression recognition using a new image representation and multiple feature fusion
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Integration of multi-feature fusion and dictionary learning for face recognition
Image and Vision Computing
Hi-index | 0.02 |
This correspondence presents a novel face recognition method that extracts multiple features in the color image discriminant (CID] color space, where three new color component images, Dl, D2, and D3, are derived using an iterative algorithm. As different color component images in the CID color space display different characteristics, three different image encoding methods are presented to effectively extract features from the component images for enhancing pattern recognition performance. To further improve classification performance, the similarity scores due to the three color component images are fused for the final decision making. Experimental results using two large-scale face databases, namely, the face recognition grand challenge (FRGC] version 2 database and the FERET database, show the effectiveness of the proposed method.