Person identification using automatic integration of speech, lip, and face experts
WBMA '03 Proceedings of the 2003 ACM SIGMM workshop on Biometrics methods and applications
A tutorial on text-independent speaker verification
EURASIP Journal on Applied Signal Processing
A Bayesian approach to audio-visual speaker identification
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Face detection based on multi-block LBP representation
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
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In this paper, we consider the problem of speaker verification as a two-class object detection problem in computer vision, where the object instances are 1-D short-time spectral vectors obtained from the speech signal. More precisely, we investigate the general problem of speaker verification in the presence of additive white Gaussian noise, which we consider as analogous to visual object detection under varying illumination conditions. Inspired by their recent success in illumination-robust object detection, we apply a certain class of binary-valued pixel-pair based features called Ferns for noise-robust speaker verification. Intensive experiments on a benchmark database according to a standard evaluation protocol have shown the advantage of the proposed features in the presence of moderate to extremely high amounts of additive noise.