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Color image discriminant models and algorithms for face recognition.

Jian Yang1, Chengjun Liu

  • 1Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102, USA. csjyang@njit.edu

IEEE Transactions on Neural Networks
|December 5, 2008
PubMed
Summary
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This study introduces a novel Color Image Discriminant (CID) model for unified image representation and recognition. The proposed method significantly improves face recognition performance, achieving a 78.26% verification rate at a 0.1% false accept rate.

Area of Science:

  • Computer Vision
  • Machine Learning
  • Pattern Recognition

Background:

  • Existing color image recognition methods often treat representation and discrimination separately.
  • A unified framework for color image representation and recognition is needed to enhance performance.

Purpose of the Study:

  • To propose a unified framework for color image representation and recognition using a Color Image Discriminant (CID) model.
  • To develop and evaluate iterative algorithms for optimizing the CID model.
  • To extend the General CID (GCID) algorithm for improved recognition performance.

Main Methods:

  • Developed a basic CID model and its general version involving color component combination coefficients and projection basis vectors.
  • Designed iterative basic CID and general CID (GCID) algorithms for optimal solutions.

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  • Extended GCID to generate three color components for enhanced recognition.

    • Main Results:

    • The proposed CID models and algorithms were validated on the Face Recognition Grand Challenge (FRGC) database and Biometric Experimentation Environment (BEE) system.
    • Achieved a face verification rate (ROC III) of 78.26% at a 0.1% false accept rate (FAR) on the challenging FRGC version 2 Experiment 4.
    • Demonstrated the effectiveness of the GCID algorithm extension in improving recognition performance.

    Conclusions:

    • The proposed CID models offer a unified approach to color image representation and recognition.
    • The developed algorithms effectively find optimal solutions for the CID models.
    • The GCID extension shows significant promise for advancing face recognition technology.