Recently, Convolutional Neural Network (CNN) has achieved great success in face detection. However, it remains a challenging problem for the current face detection methods owing to high degree of variability in scale, pose, occlusion, expression, appearance and illumination. In this paper, we propose a novel face detection network named Dual Shot face Detector(DSFD), which inherits the architecture of SSD and introduces a Feature Enhance Module (FEM) for transferring the original feature maps to extend the single shot detector to dual shot detector. Specially, Progressive Anchor Loss (PAL) computed by using two set of anchors is adopted to effectively facilitate the features. Additionally, we propose an Improved Anchor Matching (IAM) method by integrating novel data augmentation techniques and anchor design strategy in our DSFD to provide better initialization for the regressor. Extensive experiments on popular benchmarks: WIDER FACE (easy: $0.966$, medium: $0.957$, hard: $0.904$) and FDDB ( discontinuous: $0.991$, continuous: $0.862$) demonstrate the superiority of DSFD over the state-of-the-art face detectors (e.g., PyramidBox and SRN). Code will be made available upon publication.
翻译:最近,革命神经网络(CNN)在面对面探测方面取得了巨大成功,然而,由于规模变化很大,造成、隐蔽、表达、外观和光化的程度很大,对当前的脸部探测方法来说,这仍然是一个具有挑战性的问题,在本文件中,我们提议建立一个名为“双射脸探测器”的新的脸部探测网络(DSFD),它继承了SSD的架构,并引入了一个功能增强模块(FEM),用于将原始地貌图转换为将单射线探测器扩展至双射线探测器。特别是,通过使用两套锚来计算的进步锚损失(PAL),为这些特征提供有效的便利。此外,我们提议采用改进的锁定匹配(IMM)方法,将新的数据增强技术和设计战略纳入我们的DSDFD,以便为反射线探测器提供更好的初始化。 在流行基准上进行广泛的实验:WIDER CAE(精密:0.966美元,中度:0.957美元,硬值:0.904美元)和FDB(不中断:0.991美元,连续的S-Mex-RD),将显示DRD的高度。