Speaker identification using optimal lip biometrics

Biometric identification systems rely on various features for identification. Visual mouth dynamics can aid speaker recognition where the audio modality is missing or of degraded quality. This paper proposes a method of reducing the visual feature set used in a speaker-recognition system. Geometric features extracted from the lip contour are reduced using the Minimum Redundancy Maximum Relevance (MRMR) method. It is observed that MRMR features give a best accuracy of 94.7%. A small feature set reduces computation time and storage overheads. Words from a given vocabulary are also tested for speaker recognition to check robustness of MRMR features for different words.