A Robust Speaking Face Modelling Approach Based on Multilevel Fusion

In this paper, we propose a robust face modelling approach based on multilevel fusion of 3D face biometric information with audio and visual speech information for biometric identity verification applications. The proposed approach combines the information from three audio-video based modules, namely: audio, visual speech, and 3D face and performs tri-module fusion in an automatic, unsupervised and adaptive manner, by adapting to the local performance of each module. This is done by taking the output-score based reliability estimates (confidence measures) of each of the module into account. The module weightings are determined automatically such that the reliability measure of the combined scores is maximised. To test the robustness of the proposed approach, the audio and visual speech (mouth) modalities are degraded to emulate various levels of train/test mismatch; employing additive white Gaussian noise for the audio and JPEG compression for the video signals. The results show improved fusion performance for a range of tested levels of audio and video degradation, compared to the individual module performances. Experiments on a 3D stereovision database AVOZES show that, at severe levels of audio and video mismatch, the audio, mouth, 3D face, and tri-module (audio+mouth+3D face) fusion EERs were 42.9%, 32%, 15%, and 7.3% respectively for biometric speaker identity verification application