针对非约束环境下,受姿态、遮挡、尺度变化等因素的影响,密集、分辨率较低的人脸难以检测问题,提出了一种上下文敏感的多尺度人脸检测 (CSMS) 方法。该方法引入一种结合人脸上下文信息的提取模块,通过有效地融合多感受野特征来丰富目标的判别性信息。从模型结构设计的角度出发,利用多尺度特征提取尺度专门化的特征向量,使人脸检测中尺度变化具有很好的稳健性。在训练阶段采用端到端的学习方式,并引入专注于难分负例样本的训练方法来解决小尺度目标检测中的类间不平衡问题,提高了网络对难例样本的判别能力。实验结果表明,该方法对于非约束环境下的人脸检测具有很好的稳健性,在Wider Face数据集上实现了先进的检测效果。

The dense and low-resolution face is difficult to be detected under the influence of attitude, occlusion and scale change. We propose a context-sensitive multi-scale face detection (CSMS) method to solve this problem. First, the CSMS method introduces an extraction module which combines the face context information to enrich the discriminant information by effectively fusing the features of multiple receptive fields. Secondly, from the point of view of model structure design, the CSMS method uses multi-scale features to extract scale-specific feature vectors and achieve the robust scale variety in face detection. In the training phase, the CSMS method adopts the end-to-end learning method, and introduces the training method focusing on the hard negative examples to solve the class imbalance problem in the small-scale target detection, and improves the ability of the network to distinguish the difficult examples. Experimental results show that the proposed method is robust in unconstrained environments and achieves advanced detection performance on the Wider Face dataset.