Document Type

Journal Article

Department/Unit

Department of Computer Science

Title

Learning multi-boosted hmms for lip-password based speaker verification

Language

English

Abstract

This paper proposes a concept of lip motion password (simply called lip-password hereinafter), which is composed of a password embedded in the lip movement and the underlying characteristic of lip motion. It provides a double security to a visual speaker verification system, where the speaker is verified by both of the private password information and the underlying behavioral biometrics of lip motions simultaneously. Accordingly, the target speaker saying the wrong password or an impostor who knows the correct password will be detected and rejected. To this end, we shall present a multi-boosted Hidden Markov model (HMM) learning approach to such a system. Initially, we extract a group of representative visual features to characterize each lip frame. Then, an effective lip motion segmentation algorithm is addressed to segment the lip-password sequence into a small set of distinguishable subunits. Subsequently, we integrate HMMs with boosting learning framework associated with a random subspace method and data sharing scheme to formulate a precise decision boundary for these subunits verification, featuring on high discrimination power. Finally, the lip-password, whether spoken by the target speaker with the pre-registered password or not, is identified based on all the subunit verification results learned from multi-boosted HMMs. The experimental results show that the proposed approach performs favorably compared with the state-of-the-art methods. © 2005-2012 IEEE.

Keywords

data sharing scheme, lip motion segmentation, Lip-password, multi-boosted HMMs, random subspace method

Publication Date

2014

Source Publication Title

IEEE Transactions on Information Forensics and Security

Volume

9

Issue

2

Start Page

233

End Page

246

Publisher

Institute of Electrical and Electronics Engineers

DOI

10.1109/TIFS.2013.2293025

Link to Publisher's Edition

http://dx.doi.org/10.1109/TIFS.2013.2293025

ISSN (print)

15566013

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