Abstract
In face detection, low-resolution faces, such as numerous small faces of a human group in a crowded scene, are common in dense face prediction tasks. They usually contain limited visual clues and make small faces less distinguishable from the other small objects, which poses great challenge to accurate face detection. Although deep convolutional neural network has significantly promoted the research on face detection recently, current deep face detectors rarely take into account low-resolution faces and are still vulnerable to the real-world scenarios where massive amount of low-resolution faces exist. Consequently, they usually achieve degraded performance for low-resolution face detection. In order to alleviate this problem, we develop an efficient detector termed EfficientSRFace by introducing a feature-level super-resolution reconstruction network for enhancing the feature representation capability of the model. This module plays an auxiliary role in the training process, and can be removed during the inference without increasing the inference time. Extensive experiments on public benchmarking datasets, such as FDDB and WIDER Face, show that the embedded image super-resolution module can significantly improve the detection accuracy at the cost of a small amount of additional parameters and computational overhead, while helping our model achieve competitive performance compared with the state-of-the-arts methods.
Abstract (translated)
在人脸识别中,低分辨率的面孔,例如人群场景中的大量个人小型面部,是密集面部预测任务中的常见特征。它们通常包含有限的视觉线索,使小型面部与其他小型物体难以区分,这对精确面部检测构成了巨大的挑战。尽管深度学习卷积神经网络最近极大地促进了人脸识别研究,但当前的深度面部检测器很少考虑低分辨率的面孔,并且仍然面临着大量低分辨率面部存在的实际场景的脆弱性。因此,它们通常对于低分辨率面部检测的性能表现呈下降的趋势。为了解决这一问题,我们开发了一种高效的检测器,称为EfficientSRFace,通过引入一个特征级别的超分辨率重构网络,增强模型的特征表示能力。这个模块在训练过程中发挥着辅助作用,可以在推理期间去除,而不会增加推理时间。在公共基准数据集FDDB和WIDder Face等实验中,进行了广泛的实验,结果表明,嵌入的图像超分辨率模块可以在少量的额外参数和计算开销的代价下,显著提高检测精度,同时帮助我们的模型与最先进的方法相比实现竞争性能。
URL
https://arxiv.org/abs/2306.02277
