Ren Zhengwei, Liu Xinyu, Xu Jing, Zhang Yongsheng, Fang Ming
School of Artificial Intelligence, Changchun University of Science and Technology, Changchun 130012, China.
Zhongshan Institute of Changchun University of Science and Technology, Zhongshan 528400, China.
J Imaging. 2025 Jan 13;11(1):24. doi: 10.3390/jimaging11010024.
For surveillance video management in university laboratories, issues such as occlusion and low-resolution face capture often arise. Traditional face recognition algorithms are typically static and rely heavily on clear images, resulting in inaccurate recognition for low-resolution, small-sized faces. To address the challenges of occlusion and low-resolution person identification, this paper proposes a new face recognition framework by reconstructing Retinaface-Resnet and combining it with Quality-Adaptive Margin (adaface). Currently, although there are many target detection algorithms, they all require a large amount of data for training. However, datasets for low-resolution face detection are scarce, leading to poor detection performance of the models. This paper aims to solve Retinaface's weak face recognition capability in low-resolution scenarios and its potential inaccuracies in face bounding box localization when faces are at extreme angles or partially occluded. To this end, Spatial Depth-wise Separable Convolutions are introduced. Retinaface-Resnet is designed for face detection and localization, while adaface is employed to address low-resolution face recognition by using feature norm approximation to estimate image quality and applying an adaptive margin function. Additionally, a multi-object tracking algorithm is used to solve the problem of moving occlusion. Experimental results demonstrate significant improvements, achieving an accuracy of 96.12% on the WiderFace dataset and a recognition accuracy of 84.36% in practical laboratory applications.
对于大学实验室中的监控视频管理，经常会出现遮挡和低分辨率人脸捕捉等问题。传统的人脸识别算法通常是静态的，严重依赖清晰的图像，导致对低分辨率、小尺寸人脸的识别不准确。为了应对遮挡和低分辨率人员识别的挑战，本文提出了一种新的人脸识别框架，通过重构Retinaface-Resnet并将其与质量自适应边距（adaface）相结合。目前，虽然有许多目标检测算法，但它们都需要大量数据进行训练。然而，用于低分辨率人脸检测的数据集稀缺，导致模型的检测性能较差。本文旨在解决Retinaface在低分辨率场景中人脸识别能力较弱以及当人脸处于极端角度或部分遮挡时人脸边界框定位可能不准确的问题。为此，引入了空间深度可分离卷积。Retinaface-Resnet用于人脸检测和定位，而adaface则通过使用特征范数近似来估计图像质量并应用自适应边距函数来解决低分辨率人脸识别问题。此外，使用多目标跟踪算法来解决移动遮挡问题。实验结果表明有显著改进，在WiderFace数据集上准确率达到96.12%，在实际实验室应用中的识别准确率达到84.36%。
