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Updated: Jun 24, 2026
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Published on: July 11, 2025
Color face recognition for degraded face images
Jae Young Choi1, Yong Man Ro, Konstantinos N Kostas Plataniotis
1Image and Video System Laboratory, Korea Advanced Institute of Science and Technology, Daejeon, Korea. jygchoi@kaist.ac.kr
Summary
Facial color cues significantly enhance low-resolution face recognition (FR) performance. This study introduces a metric to quantify color
Area of Science:
- Computer Vision
- Biometrics
- Pattern Recognition
Background:
- Low-resolution faces degrade performance in current face recognition (FR) systems.
- Intensity-based FR methods struggle with images below 20x20 pixels.
Purpose of the Study:
- To demonstrate the significant improvement of facial color cues over intensity-based features for low-resolution face recognition.
- To introduce a theoretical metric, variation ratio gain (VRG), to quantify the impact of color on FR performance relative to resolution.
Main Methods:
- Proposed a novel metric, variation ratio gain (VRG), for theoretical analysis.
- Conducted extensive performance evaluations using over 3000 color facial images from three standard databases.
- Tested color feature effectiveness on three subspace FR methods: eigenfaces, fisherfaces, and Bayesian.
Main Results:
- Facial color features significantly improve recognition performance compared to intensity-based features for low-resolution faces.
- VRG quantitatively characterizes the positive effect of color on FR as resolution decreases.
- Color features reduced the recognition error rate by at least an order of magnitude for faces 25x25 pixels or smaller.
Conclusions:
- Facial color information is crucial for robust face recognition in low-resolution scenarios.
- The proposed VRG metric provides a theoretical basis for understanding color's benefit in FR.
- Color-enhanced FR systems offer substantial performance gains over traditional intensity-based methods for degraded facial images.
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