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Robust Face Recognition via Multi-Scale Patch-Based Matrix Regression.

Guangwei Gao1, Jian Yang2, Xiaoyuan Jing3

  • 1Institute of Advanced Technology, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China.

Plos One
|August 16, 2016
PubMed
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This study introduces a multi-scale patch-based matrix regression for robust face recognition, effectively addressing the small sample size problem. The novel approach optimizes ensemble decisions from various patch scales, outperforming existing methods.

Area of Science:

  • Computer Vision
  • Machine Learning
  • Pattern Recognition

Background:

  • Limited training data is a fundamental challenge in real-world applications like surveillance and access control.
  • Nuclear norm-based matrix regression offers robustness for face recognition with occlusions but struggles with small sample sizes.
  • Patch-based matrix regression improves performance but optimal patch size selection remains difficult.

Purpose of the Study:

  • To develop a robust face recognition method that overcomes the small sample size problem.
  • To leverage complementary information from different patch scales for improved recognition accuracy.
  • To optimize the integration of multi-scale patch regression outputs for enhanced decision-making.

Main Methods:

  • Proposed a multi-scale patch-based matrix regression scheme.

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  • Ensemble learning approach to optimally combine outputs from various patch scales.
  • Extensive experiments on benchmark face databases for validation.

    • Main Results:

    • The proposed method demonstrates significant effectiveness and robustness in face recognition.
    • Outperforms several state-of-the-art patch-based face recognition algorithms.
    • Successfully addresses the limitations of traditional nuclear norm-based and single-scale patch-based methods.

    Conclusions:

    • The multi-scale patch-based matrix regression scheme is a superior approach for face recognition, especially with limited training data.
    • Optimal utilization of multi-scale information leads to enhanced recognition performance and robustness.
    • The method provides a promising solution for real-world biometric identification systems.