An introduction to Kolmogorov complexity and its applications (2nd ed.)
An introduction to Kolmogorov complexity and its applications (2nd ed.)
The inference of identity in forensic speaker recognition
Speech Communication - Speaker recognition and its commercial and forensic applications
Evolutionary Pursuit and Its Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine-Learning Applications of Algorithmic Randomness
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Feature-Based Face Recognition Using Mixture-Distance
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
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
Open Set Face Recognition Using Transduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Effective transductive learning via objective model selection
Pattern Recognition Letters
How effective are landmarks and their geometry for face recognition?
Computer Vision and Image Understanding
Toward Category-Level Object Recognition (Lecture Notes in Computer Science)
Toward Category-Level Object Recognition (Lecture Notes in Computer Science)
Robust Object Recognition with Cortex-Like Mechanisms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Computation
Reliable face recognition using adaptive and robust correlation filters
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Audio, Speech, and Language Processing
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
A shape- and texture-based enhanced Fisher classifier for face recognition
IEEE Transactions on Image Processing
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We describe in this paper a novel biometric methodology for face recognition suitable to address pose, illumination, and expression (PIE) image variability, temporal change, flexible matching, and last but not least occlusion and disguise that are usually referred to as denial and deception. The adverse conditions listed above affect the scope and performance of biometric analysis vis-a-vis both training and testing. The conceptual framework proposed here draws support from discriminative methods using likelihood ratios. At the conceptual level it links forensics and biometrics, while at the implementation level it links the Bayesian framework and statistical learning theory. As many of the concerns listed usually affect only parts of the face, a non-parametric recognition-by-part approach is advanced here for the purpose of reliable face recognition. Recognition-by-parts facilitates authentication because it does not seek for explicit invariance. Instead, it handles variability using component-based configurations that are flexible enough to compensate among others for limited pose changes, if any, and limited occlusion and disguise. The recognition-by-parts approach proposed here supports incremental and progressive processing. It is similar in nature to modern linguistics and practical intelligence with the emphasis on semantics and pragmatics. Layered categorization starts with face detection using implicit rather than explicit segmentation. It proceeds with face authentication that involves feature selection of local patch instances including dimensionality reduction, exemplar-based clustering of patches into parts, and data fusion for matching using boosting driven by parts that play the role of weak learners. The implementation, driven by transduction, employs proximity and typicality (ranking) realized using strangeness and random deficiency p-values, respectively. The feasibility and reliability of the proposed architecture has been validated using FERET and FRGC data. The paper concludes with suggestions for augmenting and enhancing the scope and utility of the recognition-by-parts architecture.