›› 2015, Vol. 30 ›› Issue (3): 499-510.doi: 10.1007/s11390-015-1540-3

Special Issue: Artificial Intelligence and Pattern Recognition; Computer Graphics and Multimedia

• Special Section on Computational Visual Media • Previous Articles     Next Articles

Facial similarity learning with humans in the loop

Chong Cao(曹翀), Student Member, CCF, Hai-Zhou Ai(艾海舟), Senior Member, IEEE, Member, CCF   

  1. Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology Tsinghua University, Beijing 100084, China
  • Received:2014-11-28 Revised:2015-03-07 Online:2015-05-05 Published:2015-05-05
  • About author:Chong Cao received her B.S. degree in computer science and technology from Tsinghua University, Beijing, in 2010. She is currently a Ph.D. candidate at Tsinghua University. Her research interests include computer vision, pattern recognition and multimedia, with special focus on face retrieval and facial similarity learning.
  • Supported by:

    This work was partly supported by the National Basic Research 973 Program of China under Grant No. 2011CB302203.

Similarity learning has always been a popular topic in computer vision research. Among this, facial similarity is especially important and difficult due to its wide applications and the nonrigid nature of human faces. The large gap between feature representations and human perceptual descriptions makes the problem even harder. In this paper, we learn facial similarity through human-computer interactions. To learn perceptual similarities of faces in a gallery set, we ask users to label some candidate images with their similarities to a probe image. Based on users' responses, a sampling algorithm actively generates a probe image and a set of candidates for the next query. Assisted with human efforts, the algorithm embeds all the images in a space where the distance between two subjects conforms to their dissimilarity in human perception. We apply the learned embedding to face retrieval and compare our method to some feature-based methods on a dataset we collect from social network sites (SNS). Experimental results demonstrate that incorporating human efforts can ensure retrieval accuracy. At the same time, the active sampling algorithm reduces human efforts.

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