Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
On combining classifiers using sum and product rules
Pattern Recognition Letters
The M2VTS Multimodal Face Database (Release 1.00)
AVBPA '97 Proceedings of the First International Conference on Audio- and Video-Based Biometric Person Authentication
Comparative Evaluation of Face Sequence Matching for Content-Based Video Access
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Evaluation and analysis of a face and voice outdoor multi-biometric system
Pattern Recognition Letters
2D and 3D face recognition: A survey
Pattern Recognition Letters
Damascening video databases for evaluation of face tracking and recognition - The DXM2VTS database
Pattern Recognition Letters
Journal of Cognitive Neuroscience
Real time face and mouth recognition using radial basis function neural networks
Expert Systems with Applications: An International Journal
Audio-guided video-based face recognition
IEEE Transactions on Circuits and Systems for Video Technology
Dual optimal multiband features for face recognition
Expert Systems with Applications: An International Journal
A weighted probabilistic approach to face recognition from multiple images and video sequences
Image and Vision Computing
The BANCA database and evaluation protocol
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
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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Audio-visual recognition system is becoming popular because it overcomes certain problems of traditional audio-only recognition system. However, difficulties due to visual variations in video sequence can significantly degrade the recognition performance of the system. This problem can be further complicated when more than one visual variation happen at the same time. Although several databases have been created in this area, none of them includes realistic visual variations in video sequence. With the aim to facilitate the development of robust audio-visual recognition systems, the new audio-visual UNMC-VIER database is created. This database contains various visual variations including illumination, facial expression, head pose, and image resolution variations. The most unique aspect of this database is that it includes more than one visual variation in the same video recording. For the audio part, the utterances are spoken in slow and normal speech pace to improve the learning process of audio-visual speech recognition system. Hence, this database is useful for the development of robust audio-visual person, speech recognition and face recognition systems.