Abstract In this paper we propose fusion of shape and texture information from 3D face models of persons with the acoustic features extracted from spoken utterances, to improve the performance against imposter and replay attacks. Experiments conducted on two multimodal speaking face corpora, VidTIMIT and AVOZES, allowed less than 2 % EERs to be achieved for imposter attacks, and less than 1% for type-1 replay attacks for multimodal feature fusion of acoustic, shape and texture features. For type-2 replay attacks, more difficult type of spoof attacks, less than 7% EER was achieved.
Face-Voice Authentication Based on 3D Face Models
Computer Vision – ACCV 2006 ; 5 ; 559-568
Lecture Notes in Computer Science ; 3851 , 5
2006-01-01
10 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Acoustic Feature , Equal Error Rate , Replay Attack , Feature Fusion , Late Fusion Computer Science , Computer Imaging, Vision, Pattern Recognition and Graphics , Pattern Recognition , Image Processing and Computer Vision , Artificial Intelligence (incl. Robotics) , Algorithm Analysis and Problem Complexity
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