DocumentCode
940979
Title
Audio-Visual Biometrics
Author
Aleksic, Petar S. ; Katsaggelos, Aggelos K.
Author_Institution
Dept. of Electr. Eng. & Comput. Sci., Northwestern Univ., Evanston, IL
Volume
94
Issue
11
fYear
2006
Firstpage
2025
Lastpage
2044
Abstract
Biometric characteristics can be utilized in order to enable reliable and robust-to-impostor-attacks person recognition. Speaker recognition technology is commonly utilized in various systems enabling natural human computer interaction. The majority of the speaker recognition systems rely only on acoustic information, ignoring the visual modality. However, visual information conveys correlated and complimentary information to the audio information and its integration into a recognition system can potentially increase the system´s performance, especially in the presence of adverse acoustic conditions. Acoustic and visual biometric signals, such as the person´s voice and face, can be obtained using unobtrusive and user-friendly procedures and low-cost sensors. Developing unobtrusive biometric systems makes biometric technology more socially acceptable and accelerates its integration into every day life. In this paper, we describe the main components of audio-visual biometric systems, review existing systems and their performance, and discuss future research and development directions in this area
Keywords
acoustics; biometrics (access control); face recognition; feature extraction; man-machine systems; speech recognition; acoustic information; audio-visual biometrics; feature extraction; natural human computer interaction; person recognition; speaker recognition technology; visual information; visual modality; Acceleration; Acoustic sensors; Biometrics; Biosensors; Character recognition; Human computer interaction; Research and development; Robustness; Speaker recognition; System performance; Audio-visual biometrics; audio-visual databases; audio-visual fusion; audio-visual person recognition; face tracking; hidden Markov models; multimodal recognition; visual feature extraction;
fLanguage
English
Journal_Title
Proceedings of the IEEE
Publisher
ieee
ISSN
0018-9219
Type
jour
DOI
10.1109/JPROC.2006.886017
Filename
4052464
Link To Document