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University of Balamand > Academics > Research > Seminars > Walid Karam

Audio Visual Imposture

SUBMITTED BY: Mr. Walid Karam
Faculty of Sciences
University of Balamand

ABSTRACT: A GMM based audio visual speaker verification system is described and an Active Appearance Model with a linear regression speaker transformation system is used to evaluate the robustness of the verification. An Active Appearance Model (AAM) is used to automatically locate and track a speaker’s face in a video recording. A Gaussian Mixture Model (GMM) based classifier (BECARS) is used for face verification. GMM training and testing is accomplished on DCT based extracted features of the detected faces. On the audio side, speech features are extracted and used for speaker verification with the GMM based classifier. Fusion of both audio and video modalities for audio visual speaker verification is compared with face verification and speaker verification systems.

To improve the robustness of the multimodal biometric identity verification system, an audio visual imposture system is envisioned. It consists of an automatic voice transformation technique that an impostor may use to assume the identity of an authorized client. Features of the transformed voice are then combined with the corresponding appearance features and fed into the GMM based system BECARS for training. This combined audio visual model is used for the detection of the absence of perfect audio / video synchronization in test video sequences that an impostor may have reproduced.

Experiments are being conducted on the BANCA database, with a prospect of experimenting on the newly developed PDAtabase developed within the scope of the SecurePhone project.
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University of Balamand,
Balamand Al Kurah,
Lebanon

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