Security Based Speaker Verification for Lip-Password using Learning Multi-Boosted HMMS

Lip password is composed of a password embedded with motions of lip and point out the characteristic of lip motion. To provides security of a speaker verification system by using private password and behavioral biometrics of a lip motion simultaneously. The target speaker saying wrong password then rejected and the target speaker saying correct password then detected. Here a Hidden Markov Model (HMM) learning approach based on multi boosted scheme is presented for a security speaker system.  This method first extract the visual features and to characterize each frame. The lip password segmentation algorithm is used for the segmentation of lip sequences. Hidden Markov Models with boosting learning framework contains random subspace method and data sharing scheme. Finally, the lip-password is verified based on verification results provided by all the subunit learned from HMM based multi-boosted scheme and it will check whether the password is spoken by the speaker with the already-recorded password or not.