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2013 IEEE Conference on Computer Vision and Pattern Recognition (2013)
Portland, OR, USA USA
June 23, 2013 to June 28, 2013
ISSN: 1063-6919
pp: 3610-3617
ABSTRACT
This paper considers the person verification problem in modern surveillance and video retrieval systems. The problem is to identify whether a pair of face or human body images is about the same person, even if the person is not seen before. Traditional methods usually look for a distance (or similarity) measure between images (e.g., by metric learning algorithms), and make decisions based on a fixed threshold. We show that this is nevertheless insufficient and sub-optimal for the verification problem. This paper proposes to learn a decision function for verification that can be viewed as a joint model of a distance metric and a locally adaptive thresholding rule. We further formulate the inference on our decision function as a second-order large-margin regularization problem, and provide an efficient algorithm in its dual from. We evaluate our algorithm on both human body verification and face verification problems. Our method outperforms not only the classical metric learning algorithm including LMNN and ITML, but also the state-of-the-art in the computer vision community.
INDEX TERMS
Pedestrian Re-identification, Person Verification, Face Verification
CITATION

L. Cao, T. S. Huang, F. Liang, S. Chang, Z. Li and J. R. Smith, "Learning Locally-Adaptive Decision Functions for Person Verification," 2013 IEEE Conference on Computer Vision and Pattern Recognition(CVPR), Portland, OR, USA USA, 2013, pp. 3610-3617.
doi:10.1109/CVPR.2013.463
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