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2016 IEEE International Conference on Multimedia and Expo (ICME) (2016)
Seattle, WA, USA
July 11, 2016 to July 15, 2016
ISSN: 1945-788X
ISBN: 978-1-4673-7259-6
pp: 1-6
Nanhai Zhang , Beijing University of Posts and Telecommunications, Beijing, China
Jiajie Han , Beijing University of Posts and Telecommunications, Beijing, China
Jiani Hu , Beijing University of Posts and Telecommunications, Beijing, China
Weihong Deng , Beijing University of Posts and Telecommunications, Beijing, China
ABSTRACT
Noticing that face images(from different persons) with high similarity computed by current state-of-the-art methods may be not visually similar, in this paper, we present a new verification problem on judging whether the given faces are similar or not. Similar to “view 2” of Labeled Faces in the Wild(LFW), we construct ten subsets' face pairs using images from LFW. Label of each pair comes from human annotation results. Since similar faces are not from the same person after all, pushing similar faces too close will easily contribute to wrong models. Therefore, we propose a new geometry-aware metric learning(GAML) method which can preserve the similarity of similar faces while enlarge the difference between dissimilar faces. Experimental results show that our method outperforms traditional face verification methods on our similar face dataset.
INDEX TERMS
Face, Measurement, Face recognition, Neural networks, Lighting, Learning systems, Silicon
CITATION

N. Zhang, J. Han, J. Hu and W. Deng, "Geometry-aware metric learning for similar face recognition," 2016 IEEE International Conference on Multimedia and Expo (ICME), Seattle, WA, USA, 2016, pp. 1-6.
doi:10.1109/ICME.2016.7552916
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