The nature of statistical learning theory
The nature of statistical learning theory
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Interpretation and Coding of Face Images Using Flexible Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Person Identification Using Multiple Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support Vector Regression and Classification Based Multi-View Face Detection and Recognition
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Noise adaptive stream weighting in audio-visual speech recognition
EURASIP Journal on Applied Signal Processing
Audio-visual speaker identification based on the use of dynamic audio and visual features
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Face recognition: a convolutional neural-network approach
IEEE Transactions on Neural Networks
Local spatiotemporal descriptors for visual recognition of spoken phrases
Proceedings of the international workshop on Human-centered multimedia
Lipreading with local spatiotemporal descriptors
IEEE Transactions on Multimedia
Audio, video and multimodal person identification in a smart room
CLEAR'06 Proceedings of the 1st international evaluation conference on Classification of events, activities and relationships
Visual processing-inspired fern-audio features for noise-robust speaker verification
Proceedings of the 2010 ACM Symposium on Applied Computing
Combining dynamic texture and structural features for speaker identification
Proceedings of the 2nd ACM workshop on Multimedia in forensics, security and intelligence
Multimodal coordination of facial action, head rotation, and eye motion during spontaneous smiles
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Robust automatic human identification using face, mouth, and acoustic information
AMFG'05 Proceedings of the Second international conference on Analysis and Modelling of Faces and Gestures
VALID: a new practical audio-visual database, and comparative results
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Histogram equalization in SVM multimodal person verification
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
A survey on multi person identification and localization
Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments
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This paper presents a multi-expert person identification system based on the integration of three separate systems employing audio features, static face images and lip motion features respectively. Audio person identification was carried out using a text dependent Hidden Markov Model methodology. Modeling of the lip motion was carried out using Gaussian probability density functions. The static image based identification was carried out using the FaceIt system. Experiments were conducted with 251 subjects from the XM2VTS audio-visual database. Late integration using automatic weights was employed to combine the three experts. The integration strategy adapts automatically to the audio noise conditions. It was found that the integration of the three experts improved the person identification accuracies for both clean and noisy audio conditions compared with the audio only case. For audio, FaceIt, lip motion, and tri-expert identification, maximum accuracies achieved were 98%, 93.22%, 86.37% and 100% respectively. Maximum bi-expert integration of the two visual experts achieved an identification accuracy of 96.8% which is comparable to the best audio accuracy of 98%.