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Updated: Apr 30, 2026

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

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Published on: June 18, 2021

Sparse tensor discriminant color space for face verification.

Su-Jing Wang, Jian Yang, Ming-Fang Sun

IEEE Transactions on Neural Networks and Learning Systems

|May 9, 2014

Summary

This summary is machine-generated.

This study introduces a novel sparse tensor discriminant color space (STDCS) model for improved face recognition. The STDCS model enhances performance and robustness compared to existing methods.

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Published on: November 28, 2025

Area of Science:

Computer Vision
Machine Learning
Image Processing

Background:

Color is crucial for face recognition, but optimal color spaces vary by task.
Selecting an effective color space for face recognition remains a challenge.

Purpose of the Study:

To propose a novel Sparse Tensor Discriminant Color Space (STDCS) model for face recognition.
To enhance the robustness and interpretability of color-based face recognition systems.

Main Methods:

Representing color images as third-order tensors.
Transforming eigenvalue problems into regression problems.
Applying Lasso or Elastic Net for sparse matrix computation.

Main Results:

The STDCS model preserves spatial structure and enhances robustness.
Experiments on AR, Georgia Tech, and Labeled Faces in the Wild datasets demonstrate superior performance.
The proposed method outperforms the state-of-the-art TDCS model.

Conclusions:

The STDCS model offers a robust and effective approach to color space selection for face recognition.
This method provides better performance and robustness than previous techniques.