We propose a deep convolutional neural network (CNN) for face detection
leveraging on facial attributes based supervision. We observe a phenomenon that
part detectors emerge within CNN trained to classify attributes from uncropped
face images, without any explicit part supervision. The observation motivates a
new method for finding faces through scoring facial parts responses by their
spatial structure and arrangement. The scoring mechanism is data-driven, and
carefully formulated considering challenging cases where faces are only
partially visible. This consideration allows our network to detect faces under
severe occlusion and unconstrained pose variations. Our method achieves
promising performance on popular benchmarks including FDDB, PASCAL Faces, AFW,
and WIDER FACE.

本文提出了一种基于面部属性监督的深度卷积神经网络 (CNN) 进行人脸检测的方法，通过观察 CNN 在对未裁剪的面部图像进行属性分类训练时产生局部探测器的现象，并利用具有局部结构和排列的人脸部分响应的评分机制来寻找人脸，使得该网络能够在面部部分可见的严重遮挡和非约束姿态变化下检测出人脸，实现了在 FDDB、PASCAL Faces、AFW 和 WIDER FACE 等流行基准测试中有着很有希望的性能表现。