Adaptive face detection algorithms in blurring scenarios

Yi-Wei Tsai; Gan-Chee Kim; Jian-Jiun Ding

doi:10.1117/12.3057554

5 February 2025 Adaptive face detection algorithms in blurring scenarios

Yi-Wei Tsai, Gan-Chee Kim, Jian-Jiun Ding

Author Affiliations +

Proceedings Volume 13510, International Workshop on Advanced Imaging Technology (IWAIT) 2025; 135100G (2025) https://doi.org/10.1117/12.3057554
Event: International Workshop on Advanced Imaging Technology (IWAIT) 2025, 2025, Douliu City, Taiwan

Abstract

Face detection is a very important process in facial image processing. There are many existing face detection algorithms, however, we observe that there are still a lot of room for improvement in the blurred scenario, since blurred faces have much fewer meaningful features compared to clear ones. In this work, we propose a detection framework for blurred faces using several image processing techniques. First, multiple facial images with different extents and approaches of blurriness are generated for the training and validation sets. With them, several neural network models with different architectures, including YOLO and the DenseNet, are trained. Finally, some geometric and color relationships are examined in order to eliminate the redundant face candidates. Moreover, we also conduct an experiment that involves ensemble learning. The experimental results show that our method is superior to the state-of-the-art face detection methods in dealing with blurred faces, and we can boost the overall performance for face detection effectively.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yi-Wei Tsai, Gan-Chee Kim, and Jian-Jiun Ding "Adaptive face detection algorithms in blurring scenarios", Proc. SPIE 13510, International Workshop on Advanced Imaging Technology (IWAIT) 2025, 135100G (5 February 2025); https://doi.org/10.1117/12.3057554

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KEYWORDS

Facial recognition systems

Object detection

Education and training

Detection and tracking algorithms

Chromium

Tunable filters

Image sharpness

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