Abstract
Face detection is a crucial component in many AI-driven applications such as surveillance, biometric authentication, and human-computer interaction. However, real-world conditions like low-resolution imagery present significant challenges that degrade detection performance. In this study, we systematically investigate the impact of input resolution on the accuracy and robustness of three prominent deep learning-based face detectors: YOLOv11, YOLOv12, and MTCNN. Using the WIDER FACE dataset, we conduct extensive evaluations across multiple image resolutions (160x160, 320x320, and 640x640) and assess each model's performance using metrics such as precision, recall, mAP50, mAP50-95, and inference time. Results indicate that YOLOv11 outperforms YOLOv12 and MTCNN in terms of detection accuracy, especially at higher resolutions, while YOLOv12 exhibits slightly better recall. MTCNN, although competitive in landmark localization, lags in real-time inference speed. Our findings provide actionable insights for selecting resolution-aware face detection models suitable for varying operational constraints.
Abstract (translated)
面部检测是许多人工智能驱动应用（如监控、生物识别认证和人机交互）的关键组成部分。然而，现实条件下的低分辨率图像对检测性能构成了重大挑战。在这项研究中，我们系统地调查了输入分辨率对三种基于深度学习的面部检测器——YOLOv11、YOLOv12 和 MTCNN 的准确性和鲁棒性的影响。使用 WIDER FACE 数据集，在多个图像分辨率（160x160、320x320 和 640x640）下进行了广泛的评估，并采用包括精度、召回率、mAP50、mAP50-95 及推断时间等指标来评估每个模型的性能。结果表明，YOLOv11 在检测准确性方面优于 YOLOv12 和 MTCNN，特别是在较高分辨率下，而 YOLOv12 的召回率略高。MTCNN 尽管在地标定位上具有竞争力，但在实时推断速度方面落后。我们的发现为选择适合各种操作约束的分辨率感知面部检测模型提供了可操作的见解。
URL
https://arxiv.org/abs/2507.23341
