Speaker identification system based on lip-motion feature

Abstract

Traditional lip features have been used in speech recognition, but lately they have also been found useful as a new biometric identifier in computer vision applications. Firstly, we locate lips according to geometric distribution of human faces. Then, we propose an algorithm for extracting representative frame pictures based on gray changes during speech. Scale-invariant feature transform (SIFT) feature is introduced into speaker identification system. Based on Sift algorithm, we extract lip feature including texture and motion information, which can well describe lip deformation progress during speech. Finally, this paper presents a simple classification algorithm by comparing the ratio of eigenvalue to the reference value. Compared with local binary model (LBP) feature and histogram of oriented gradients (HOG) feature, experimental results show that the improved algorithm of feature extraction and classification can work effectively and achieve a satisfactory performance.