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Related Experiment Video

Updated: Apr 30, 2026

Quantification of Orofacial Phenotypes in Xenopus
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Published on: November 6, 2014

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Sparse representation classifier steered discriminative projection with applications to face recognition.

Jian Yang, Delin Chu, Lei Zhang

    IEEE Transactions on Neural Networks and Learning Systems
    |May 9, 2014
    PubMed
    Summary

    A new method, Sparse Representation-based Classifier-steered Discriminative Projection (SRC-DP), enhances face recognition. SRC-DP improves accuracy and efficiency by optimizing feature extraction for Sparse Representation-based Classification (SRC).

    Related Experiment Videos

    Last Updated: Apr 30, 2026

    Quantification of Orofacial Phenotypes in Xenopus
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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Pattern Recognition

    Background:

    • Sparse Representation-based Classification (SRC) shows promise for face recognition.
    • Existing feature extraction methods may not be optimal for SRC.

    Purpose of the Study:

    • To develop a dimensionality reduction method tailored for SRC.
    • To improve the performance and efficiency of SRC-based face recognition systems.

    Main Methods:

    • Introduced SRC-steered Discriminative Projection (SRC-DP) for feature extraction.
    • SRC-DP maximizes the ratio of between-class to within-class reconstruction residuals.
    • Applied SRC-DP to reduce dimensionality for face representation.

    Main Results:

    • SRC-DP significantly enhances SRC performance in face recognition.
    • The method provides efficient low-dimensional face representations.
    • Experiments on AR, Extended Yale B, and PIE databases confirm effectiveness.

    Conclusions:

    • SRC-DP is a highly effective feature extraction method for SRC-based face recognition.
    • The proposed approach leads to more efficient and accurate face recognition systems.