Joint Audio-Video Processing for Robust Biometric Speaker Identification in Car
Engin Erzin, Y. Yemez, Ahmet Murat Tekalp · Kluwer Academic Publishers eBooks · 2005
In this chapter, we present our recent results on the multilevel Bayesian decision fusion scheme for multimodal audio-visual speaker identification problem. The objective is to improve the recognition performance over conventional decision fusion schemes. The proposed system decomposes the information existing in a video stream into three components: speech, lip trace and face texture. Lip trace features are extracted based on 2D-DCT transform of the successive active lip frames. The mel-frequency cepstral coefficients (MFCC) of the corresponding speech signal are extracted in parallel to the lip features. The resulting two parallel and synchronous feature vectors are used to train and test a two stream Hidden Markov Model (HMM) based identification system. Face texture images are treated separately in eigenface domain and integrated to the system through decision-fusion. Reliability based ordering in multilevel decision fusion is observed to be significantly robust at all SNR levels.