Generative and discriminative models for person verification and efficient search

Li, Zhen

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

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

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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