Abstract
In recent years, deep convolutional neural networks (CNN) have significantly advanced face detection. In particular, lightweight CNNbased architectures have achieved great success due to their lowcomplexity structure facilitating real-time detection tasks. However, current lightweight CNN-based face detectors trading accuracy for efficiency have inadequate capability in handling insufficient feature representation, faces with unbalanced aspect ratios and occlusion. Consequently, they exhibit deteriorated performance far lagging behind the deep heavy detectors. To achieve efficient face detection without sacrificing accuracy, we design an efficient deep face detector termed EfficientFace in this study, which contains three modules for feature enhancement. To begin with, we design a novel cross-scale feature fusion strategy to facilitate bottom-up information propagation, such that fusing low-level and highlevel features is further strengthened. Besides, this is conducive to estimating the locations of faces and enhancing the descriptive power of face features. Secondly, we introduce a Receptive Field Enhancement module to consider faces with various aspect ratios. Thirdly, we add an Attention Mechanism module for improving the representational capability of occluded faces. We have evaluated EfficientFace on four public benchmarks and experimental results demonstrate the appealing performance of our method. In particular, our model respectively achieves 95.1% (Easy), 94.0% (Medium) and 90.1% (Hard) on validation set of WIDER Face dataset, which is competitive with heavyweight models with only 1/15 computational costs of the state-of-the-art MogFace detector.
Abstract (translated)
近年来,深度卷积神经网络(CNN)已经显著推动了人脸识别技术的进步。特别是,基于轻量级CNN架构的设计已经取得了巨大的成功,因为其低复杂性结构 facilitate 实时检测任务。然而,当前基于轻量级CNN的人脸识别技术在处理不足特征表示、具有不均衡 aspect ratios 和遮挡面容等问题时存在不足。因此,它们的表现远远落后于深度重载检测器。为了在不牺牲准确性的前提下实现高效的人脸识别,我们在本研究中设计了名为EfficientFace 高效的深度人脸识别器,它包含三个特征增强模块。首先,我们设计了一种跨尺度特征融合策略,以促进自下而上的信息传播,从而进一步加强了低级别和高级别特征的融合。此外,这有助于估计面部的位置和增强面部特征的描述能力。其次,我们引入了一个感知场增强模块,考虑各种 aspect ratios 的面部。第三,我们添加了一个注意力机制模块,以提高遮挡面容的表示能力。我们评估了EfficientFace 在不同公共基准上的性能,实验结果证明了我们方法的 appealing 表现。特别是,我们的模型在WIDE Face 数据集的验证集上分别实现了95.1%(简单)、94.0%(中等)、90.1%(困难),这比最先进的mogFace检测器的计算成本仅高出1/15。
URL
https://arxiv.org/abs/2302.11816
