Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Person Identification Using Multiple Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Face Detection Using the Hausdorff Distance
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
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
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
Journal of Cognitive Neuroscience
The AIT Multimodal Person Identification System for CLEAR 2007
Multimodal Technologies for Perception of Humans
Robust multimodal audio---visual processing for advanced context awareness in smart spaces
Personal and Ubiquitous Computing
Where and Who? Person Tracking and Recognition System
Proceedings of the 2007 conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies
The 2006 athens information technology speech activity detection and speaker diarization systems
MLMI'06 Proceedings of the Third international conference on Machine Learning for Multimodal Interaction
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In this paper the person identification system developed at Athens Information Technology is presented. 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 the Mel-Frequency Cepstral Coefficients of speech. Video recognition is based on linear subspace projection methods and temporal fusion using weighted voting on the results. Audiovisual fusion is done by fusing the unimodal identities into the multimodal one, using a suitable confidence metric for the results of the unimodal classifiers.