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Texture-Guided Transfer Learning for Low-Quality Face Recognition.

Meng Zhang, Rujie Liu, Daisuke Deguchi

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|November 30, 2023

Summary

This study introduces a novel texture-guided transfer learning method for improving low-quality face recognition. The approach uses backward propagation gradients to enhance feature alignment, significantly boosting accuracy in challenging surveillance scenarios.

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Area of Science:

Computer Science
Artificial Intelligence
Biometrics

Background:

Deep learning has advanced face recognition, but low-quality images remain a significant challenge, particularly in unconstrained surveillance.
Existing methods struggle with feature discrepancies between high- and low-quality face images.

Purpose of the Study:

To enhance low-quality face recognition performance using a novel texture-guided transfer learning approach.
To address the limitations of current knowledge distillation techniques in handling image quality variations.

Main Methods:

Proposed a texture-guided (TG) transfer learning method within a knowledge distillation framework.
Utilized backward propagation gradient texture for aligning low-quality image features with high-quality counterparts.
Introduced soft-attention (SA) to create a SA-TG model, focusing on informative facial regions.

Main Results:

Demonstrated superior performance on benchmark low-quality face datasets (TinyFace and QMUL-SurFace).
Achieved over 6.6% Rank-1 accuracy improvement on the TinyFace dataset.
The proposed method effectively reduces feature discrepancy between high- and low-quality images.

Conclusions:

The texture-guided transfer learning approach, particularly with soft-attention, significantly improves low-quality face recognition.
Backward propagation gradient texture offers a novel and effective mechanism for knowledge distillation in face recognition.
The method shows strong potential for real-world applications in surveillance and other low-quality image scenarios.