Abstract: Aiming at the problem that the existing models rarely designed face features and the face features were weak in discrimination, we proposed a deep face representation algorithm based on dual attention mechanism. The algorithm adopted network structure of dual attention mechanism, designed low-level features through detail attention mechanism, and paid attention to local features through automatic and adaptive learning of hierarchical features, semantic attention mechanism was used to design high-level features, and pay attention to semantic features through adaptive semantic grouping. The experimental results on LFW,YTF,MegaFace,IJB-B and IJB-C datasets show that the recognition accuracy of the dual attention mechanism method is as high as 99.87%,97.9%,98.91%,95.02% and 96.28% respectively, which is 0.02%,0.1%,0.2%,1% and 1% higher than that of similar algorithm Groupface. The comparative experiments show  the advantages of dual attention mechanism network.

Key words: machine vision, face recognition, feature representation, attention mechanism