The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Boosting for Fast Face Recognition
RATFG-RTS '01 Proceedings of the IEEE ICCV Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems (RATFG-RTS'01)
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
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
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
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Face Identification by a Cascade of Rejection Classifiers
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Evolving Effective Color Features for Improving FRGC Baseline Performance
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
UMD Experiments with FRGC Data
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Face recognition from a single image per person: A survey
Pattern Recognition
Efficient Sparse Kernel Feature Extraction Based on Partial Least Squares
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Discriminative Appearance-Based Models Using Partial Least Squares
SIBGRAPI '09 Proceedings of the 2009 XXII Brazilian Symposium on Computer Graphics and Image Processing
Enhanced local texture feature sets for face recognition under difficult lighting conditions
AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
Single image subspace for face recognition
AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
Fusing Gabor and LBP feature sets for kernel-based face recognition
AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
Face recognition under varying lighting conditions using self quotient image
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Overview and recent advances in partial least squares
SLSFS'05 Proceedings of the 2005 international conference on Subspace, Latent Structure and Feature Selection
IEEE Transactions on Image Processing
A Comparative Study of Local Matching Approach for Face Recognition
IEEE Transactions on Image Processing
Local response context applied to pedestrian detection
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Robust pose invariant face recognition using coupled latent space discriminant analysis
Computer Vision and Image Understanding
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Finding happiest moments in a social context
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Block LBP displacement based local matching approach for human face recognition
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume Part I
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The problem of face identification has received significant attention over the years. For a given probe face, the goal of face identification is to match this unknown face against a gallery of known people. Due to the availability of large amounts of data acquired in a variety of conditions, techniques that are both robust to uncontrolled acquisition conditions and scalable to large gallery sizes, which may need to be incrementally built, are challenges. In this work we tackle both problems. Initially, we propose a novel approach to robust face identification based on Partial Least Squares (PLS) to perform multi-channel feature weighting. Then, we extend the method to a tree-based discriminative structure aiming at reducing the time required to evaluate novel probe samples. The method is evaluated through experiments on FERET and FRGC datasets. In most of the comparisons our method outperforms state-of- art face identification techniques. Furthermore, our method presents scalability to large datasets.