Paper Info
Title
A Low-Complexity Dynamic Face-Voice Feature Fusion Approach to Multimodal Person Recognition
Abstract
In this paper, we show the importance of face-voice correlation for audio-visual person recognition. We evaluate the performance of a system which uses the correlation between audio-visual features during speech against audio-only, video-only and audio-visual systems which use audio and visual features independently neglecting the interdependency of a person's spoken utterance and the associated facial movements. Experiments performed on the Vid-TIMIT dataset show that the proposed multimodal scheme has lower error rate than all other comparison conditions and is more robust against replay attacks. The simplicity of the fusion technique also allows the use of only one classifier which greatly simplifies system design and allows for a simple real-time DSP implementation.
Year
DOI
Venue
2009
10.1109/ISM.2009.78
ISM
Keywords
Field
DocType
audio-visual system,multimodal person recognition,low-complexity dynamic face-voice feature,audio-visual feature,face-voice correlation,simplifies system design,comparison condition,fusion approach,facial movement,fusion technique,vid-timit dataset show,audio-visual person recognition,lower error rate,multimodal,face recognition,replay attack,system design,accuracy,robustness,sensor fusion,biometric,hidden markov models,face,feature extraction,gesture recognition,error rate,speaker recognition,data mining,speaker,image classification,real time
Computer science,Gesture recognition,Robustness (computer science),Speaker recognition,Artificial intelligence,Facial recognition system,Computer vision,Pattern recognition,Word error rate,Feature extraction,Sensor fusion,Speech recognition,Hidden Markov model
Conference
Citations 
PageRank 
References 
2
0.36
6
Authors
3
Name
Order
Citations
PageRank
Dhaval Shah1239.25
Kyu Jeong Han2859.10
Narayanan Shrikanth35558439.23