Unconstrained Face Recognition

  • Shaohua Kevin Zhou
  • Rama Chellappa
  • Wenyi Zhao

Part of the International Series on Biometrics book series (KISB, volume 5)

Table of contents

  1. Front Matter
    Pages i-xii
  2. Fundamentals, Preliminaries and Reviews

    1. Front Matter
      Pages 1-1
    2. Pages 3-16
  3. Face Recognition Under Variations

    1. Front Matter
      Pages 43-43
    2. Pages 111-127
  4. Face Recognition Via Kernel Learning

  5. Face Tracking and Recognition from Videos

    1. Front Matter
      Pages 157-157
    2. Pages 159-175
  6. Summary and Future Research Directions

    1. Front Matter
      Pages 215-215
  7. Back Matter
    Pages 225-244

About this book


Although face recognition has been actively studied over the past decade, the state-of-the-art recognition systems yield satisfactory performance only under controlled scenarios. Recognition accuracy degrades significantly when confronted with unconstrained situations. Examples of unconstrained conditions include illumination and pose variations, video sequences, expression, aging, and so on. Recently, researchers have begun to investigate face recognition under unconstrained conditions that is referred to as unconstrained face recognition.

This volume provides a comprehensive view of unconstrained face recognition, especially face recognition from multiple still images and/or video sequences, assembling a collection of novel approaches able to recognize human faces under various unconstrained situations. The underlying basis of these approaches is that, unlike conventional face recognition algorithms, they exploit the inherent characteristics of the unconstrained situation and thus improve the recognition performance when compared with conventional algorithms. Unconstrained Face Recognition is accessible to a wide audience with an elementary level of linear algebra, probability and statistics, and signal processing.

Unconstrained Face Recognition is designed primarily for a professional audience composed of practitioners and researchers working within face recognition and other biometrics. Also instructors can use the book as a textbook or supplementary reading material for graduate courses on biometric recognition, human perception, computer vision, or other relevant seminars.


algorithms computer science performance tracking video

Authors and affiliations

  • Shaohua Kevin Zhou
    • 1
  • Rama Chellappa
    • 2
  • Wenyi Zhao
    • 3
  1. 1.Integrated Data Systems Dept.Siemens Corporate ResearchPrinceton
  2. 2.Center Automation ResearchUniv. Maryland College ParkCollege Park
  3. 3.Vision Technologies LabSarnoff corp.Princeton

Bibliographic information

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