  Models based on self-attention mechanisms have been successful in analyzing
temporal data and have been widely used in the natural language domain. We
propose a new model architecture for video face representation and recognition
based on a self-attention mechanism. Our approach could be used for video with
single and multiple identities. To the best of our knowledge, no one has
explored the aggregation approaches that consider the video with multiple
identities. The proposed approach utilizes existing models to get the face
representation for each video frame, e.g., ArcFace and MobileFaceNet, and the
aggregation module produces the aggregated face representation vector for video
by taking into consideration the order of frames and their quality scores. We
demonstrate empirical results on a public dataset for video face recognition
called IJB-C to indicate that the self-attention aggregation network (SAAN)
outperforms naive average pooling. Moreover, we introduce a new multi-identity
video dataset based on the publicly available UMDFaces dataset and collected
GIFs from Giphy. We show that SAAN is capable of producing a compact face
representation for both single and multiple identities in a video. The dataset
and source code will be publicly available.
