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Multi-view gender classification using multi-resolution local binary patterns and support vector machines.

Hui-Cheng Lian1, Bao-Liang Lu

  • 1Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800 Dong Chuan Rd., Shanghai, 200240, China. lianhc@cs.sjtu.edu.cn

International Journal of Neural Systems
|January 12, 2008
PubMed
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This study introduces a new multi-resolution Local Binary Pattern (LBP) method for gender classification using facial images. The novel approach achieves high accuracy, outperforming existing methods on the CAS-PEAL face database.

Area of Science:

  • Computer Vision
  • Machine Learning
  • Biometrics

Background:

  • Facial image analysis for gender classification is crucial in biometrics.
  • Existing methods often struggle with variations in pose and illumination.
  • Representing facial images effectively requires capturing both shape and texture information.

Purpose of the Study:

  • To propose a novel method for multi-view gender classification.
  • To enhance feature extraction for facial images by incorporating multi-resolution information.
  • To achieve high classification accuracy across different facial views.

Main Methods:

  • A novel multi-resolution Local Binary Pattern (LBP) approach is proposed for feature extraction.
  • Facial images are divided into regions, and LBP histograms are extracted and concatenated.

Related Experiment Videos

  • Support Vector Machines (SVMs) are employed for the classification tasks.

    • Main Results:

    • The proposed method achieved a correct classification rate of 96.56% on the CAS-PEAL face database.
    • A cross-validation average accuracy of 95.78% was obtained, demonstrating robustness.
    • The method showed superiority over support gray faces and support Gabor faces methods.

    Conclusions:

    • The multi-resolution LBP method offers an efficient and effective approach for multi-view gender classification.
    • The fine-to-coarse description of facial images enhances classification performance.
    • The method's simplicity allows for fast feature extraction and high accuracy.