Learning Audio-Visual embedding for Wild Person Verification

It has already been observed that audio-visual embedding can be extracted from these two modalities to gain robustness for person verification. However, the aggregator that used to generate a single utterance representation from each frame does not seem to be well explored. In this article, we proposed an audio-visual network that considers aggregator from a fusion perspective. We introduced improved attentive statistics pooling for the first time in face verification. Then we find that strong correlation exists between modalities during pooling, so joint attentive pooling is proposed which contains cycle consistency to learn the implicit inter-frame weight. Finally, fuse the modality with a gated attention mechanism. All the proposed models are trained on the VoxCeleb2 dev dataset and the best system obtains 0.18\%, 0.27\%, and 0.49\% EER on three official trail lists of VoxCeleb1 respectively, which is to our knowledge the best-published results for person verification. As an analysis, visualization maps are generated to explain how this system interact between modalities.