基于改进MTCNN的非均匀弱光下人脸检测

Yufei Bao, Rong Dang
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引用次数: 1

摘要

为了提高非均匀弱光条件下人脸检测的精度,提出了一种基于光照补偿和自适应权值设置的图像预处理方法,应用于MTCNN的人脸检测算法。该算法利用光补偿理论对人脸图像进行预处理,然后在MTCNN模型中加入自适应模块,可以显著提高MTCNN在非均匀弱光条件下人脸检测的准确率和检测率,准确率达到93.37%。实验表明,在非均匀弱光条件下,该方法比原始的MTCNN方法具有更好的准确率和鲁棒性,有利于后期的人脸识别任务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Face Detection under Non-uniform Low Light Based on Improved MTCNN
In order to improve the accuracy of face detection under non-uniform low-light conditions, an image preprocessing method based on illumination compensation and adaptive weight settings are proposed to apply to MTCNN's face detection algorithm. The algorithm uses light compensation theory to preprocess the face image, and then adds an adaptive module to the MTCNN model, which can significantly improve the accuracy and detection rate of MTCNN's face detection under non-uniform low-light conditions, making the accuracy rate reach 93.37 %. Experiments show that under non-uniform low light conditions, this method has better accuracy and robustness than the original MTCNN method, which is beneficial to the later face recognition task.
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