Learning locally-adaptive decision functions for person verification

Zhen Li, Shiyu Chang, Feng Liang, Thomas S Huang, Liangliang Cao, John R. Smith

Research output: Contribution to journalConference articlepeer-review


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.

Original languageEnglish (US)
Article number6619307
Pages (from-to)3610-3617
Number of pages8
JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
StatePublished - Nov 15 2013
Event26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013 - Portland, OR, United States
Duration: Jun 23 2013Jun 28 2013


  • Face Verification
  • Pedestrian Re-identification
  • Person Verification

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition


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