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Robust Multimodal Person Recognition Using Low-Complexity Audio-Visual Feature Fusion Approaches

In this paper, we first show the importance of face-voice correlation for audio-visual person recognition. We propose a simple multimodal fusion technique which preserves the correlation between audio-visual features during speech and evaluate the performance of such a system against audio-only, video-only, and audio-visual systems which use audio and visual features neglecting the interdependency of a person's spoken utterance and the associated facial movements. Experiments performed on the VidTIMIT dataset show that the proposed multimodal fusion scheme has a lower error rate than all other comparison conditions and is more robust against replay attacks. The simplicity of the fusion technique allows for low-complexity designs for a simple low-cost real-time DSP implementation. We then discuss some problems associated with the previously proposed design and, as a solution to those problems, propose two novel classifier designs which provide more flexibility and a convenient way to represent multimodal data where each modality has different characteristics. We also show that these novel classifier designs offer superior performance in terms of both accuracy and robustness.