Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Multispace KL for Pattern Representation and Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Intelligent Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reliable Face Recognition Methods: System Design, Implementation and Evaluation (International Series on Biometrics)
Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-supervised, and Unsupervised Learning (Studies in Computational Intelligence)
Mixture of KL subspaces for relevance feedback
Multimedia Tools and Applications
Face recognition with semi-supervised learning and multiple classifiers
CIMMACS'06 Proceedings of the 5th WSEAS International Conference on Computational Intelligence, Man-Machine Systems and Cybernetics
2D face recognition based on supervised subspace learning from 3D models
Pattern Recognition
Introduction to Semi-Supervised Learning
Introduction to Semi-Supervised Learning
Assessment of time dependency in face recognition: an initial study
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
The BANCA database and evaluation protocol
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Semi-Supervised Learning
Semi-supervised PCA-Based face recognition using self-training
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Incremental threshold learning for classifier selection
Neurocomputing
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The extreme variability of faces in smart environment applications, due to continuous changes in terms of pose, illumination and subject appearance (hairstyle, make-up, etc.), requires the relevant mode of variations of the subject's faces to be encoded in the templates and to be continuously updated based on new inputs. This work proposes a new video-based template updating approach suitable for home environments where the image acquisition process is totally unconstrained but a large amount of face data is available for continuous learning. A small set of labeled images is initially used to create the templates and the updating is then totally unsupervised. Although the method is here presented in conjunction with a subspace-based face recognition approach, it can be easily adapted to deal with different kinds of face representations. A thorough performance evaluation is carried out to show the efficacy and reliability of the proposed technique.