Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition: component-based versus global approaches
Computer Vision and Image Understanding - Special issue on Face recognition
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Error Control Coding, Second Edition
Error Control Coding, Second Edition
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
Journal of Cognitive Neuroscience
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
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Recently, Facial Trait Code (FTC) was proposed for solving face recognition, and was reported with promising recognition rates. However, several simplifications in the FTC encoding make it unable to handle the most rigorous face recognition scenario in which only one facial image per individual is available for enrollment in the gallery set and the probe set includes faces under variations caused by illumination, expression, pose or misalignment. In this study, we propose the Probabilistic Facial Trait Code (PFTC) with a novel encoding scheme and a probabilistic codeword distance measure. We also proposed the Pattern-Specific Subspace Learning (PSSL) scheme that encodes and recognizes faces robustly under aforementioned variations. The proposed PFTC was evaluated and compared with state-of-the-art algorithms, including the FTC, the algorithm using sparse representation, and the one using Local Binary Pattern. Our experimental study considered factors such as the number of enrollment allowed in the gallery, the variation among gallery or probe set, and reported results for both identification and verification problems. The proposed PFTC yielded significant better recognition rates in most of the scenarios than all the states-of-the-art algorithms evaluated in this study.