2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017) (2017)
Washington, DC, DC, USA
May 30, 2017 to June 3, 2017
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/FG.2017.116
In this paper, we propose an Attention-Based Template Adaptation (termed as ABTA) algorithm for face recognition in the unconstrained environment. This ABTA algorithm can be divided into two modules, which consist of an attentionbased neural network (feature extractor module) to integrate the template features of various lengths to a single fixed length feature representation according to the attention mechanism, and a template adaptation module (transfer module) which is used to transfer the knowledge of a hold-out dataset to the test templates to improve the performance via transfer learning. The feature extractor module is invariant to the order of the images and videos and can save both memory and computation resources due to its compactness. As for the transfer module, we apply the one-shot similarity to get the scores between the test template pairs, which demonstrates its power in recent research. Our method produces results comparable to the state-of-the-art in the challenging face dataset, IJB-A.
B. Dong, Z. An, J. Lin and W. Deng, "Attention-Based Template Adaptation for Face Verification," 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017)(FG), Washington, DC, DC, USA, 2017, pp. 941-946.