Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Principal Manifolds and Probabilistic Subspaces for Visual Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Incremental Learning Method for Face Recognition under Continuous Video Stream
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Computer Vision and Image Understanding - Special issue on Face recognition
A System Identification Approach for Video-based Face Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Preliminary Face Recognition Grand Challenge Results
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Video-Based Face Recognition Evaluation in the CHIL Project - Run 1
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
CLEAR'06 Proceedings of the 1st international evaluation conference on Classification of events, activities and relationships
A decision fusion system across time and classifiers for audio-visual person identification
CLEAR'06 Proceedings of the 1st international evaluation conference on Classification of events, activities and relationships
Video-based face recognition using probabilistic appearance manifolds
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Video-based face recognition using adaptive hidden markov models
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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This paper presents the person identification system developed at Athens Information Technology and its performance in the CLEAR 2007 evaluations. The system operates on the audiovisual information (speech and faces) collected over the duration of gallery and probe videos. It comprises of an audio-only (speech), a video-only (face) and an audiovisual fusion subsystem. Audio recognition is based on the Gaussian Mixture modeling of the principal components of composite feature vectors, consisting of Mel-Frequency Cepstral Coefficients and Perceptual Linear Prediction coefficients of speech. Video recognition is based on combining three different classification algorithms: Principal Components Analysis with a modified Mahalanobis distance, sub-class Linear Discriminant Analysis (featuring automatic sub-class generation) with cosine distance and Bayesian classifier based on Gaussian modeling of intrapersonal differences. A nearest neighbor classification rule is applied. A decision fusion scheme across time and classifiers returns the video identity. The audiovisual subsystem fuses the unimodal identities into the multimodal one, using a suitable confidence metric.