University of Illinois Urbana-Champaign

Generative and discriminative models for person verification and efficient search

Li, Zhen

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Zhen_Li.pdf

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https://hdl.handle.net/2142/44478

Description

Title
Generative and discriminative models for person verification and efficient search
Author(s)
Li, Zhen
Issue Date
2013-05-24T22:17:35Z
Director of Research (if dissertation) or Advisor (if thesis)
Huang, Thomas S.
Doctoral Committee Chair(s)
Huang, Thomas S.
Committee Member(s)
  • Hasegawa-Johnson, Mark A.
  • Liang, Feng
  • Liang, Zhi-Pei
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Date of Ingest
2013-05-24T22:17:35Z
Keyword(s)
  • Person Verification
  • Efficient Person Search
Abstract
This dissertation studies 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 either model the intrapersonal and extrapersonal variations with probabilistic distributions, or 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 the resulting decisions, depending merely on pairwise image differences, are nevertheless insufficient and sub-optimal for the verification problem. In this dissertation, we study both generative and discriminative models for person verification. Both methods consider a joint model of two images in a pair, and provide a decision function of second-order form that generalizes from previous approaches. We also generalize our model to a multi-setting scenario, where environment mismatch, a major challenge in cross-setting person verification, is handled. We evaluate our algorithms on face verification and human body verification problems on a number benchmark datasets, such as Multi-PIE, LFW, CIGIT-AIS, VIPer, VIPeR, and CAVIAR4REID. Our methods outperform not only the classical Bayesian Face Recognition approach, metric learning algorithms (LMNN, ITML, etc.), but also the state-of-the-art in the computer vision community. This dissertation also considers efficient person search, a potential application of person verification in surveillance systems. To this end, we propose a general learning-to-search framework for efficient similarity search in high dimensions. Experimental results show that our approach significantly outperforms the state-of-the-art learning-to-hash methods (such as spectral hashing), as well as state-of-the-art high-dimensional search algorithms (such as LSH and k-means trees).
Graduation Semester
2013-05
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http://hdl.handle.net/2142/44478
Copyright and License Information
Copyright 2013 Zhen Li

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