Multi-scale Face Detection Based on Single Neural Network
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摘要: 人脸检测是指检测并定位输入图像中所有的人脸，并返回精确的人脸位置和大小，是目标检测的重要方向。为了解决人脸尺度多样性给人脸检测造成的困难，该文提出一种新的基于单一神经网络的特征图融合多尺度人脸检测算法。该算法在不同大小的卷积层上预测人脸，实现实时多尺度人脸检测，并通过将浅层的特征图融合引入上下文信息提高小尺寸人脸检测精度。在数据集FDDB和WIDERFACE测试结果表明，所提方法达到了先进人脸检测的水平，并且该方法去掉了框推荐过程，因此检测速度更快。在WIDERFACE难、适中、简单3个子数据集上测试结果分别为87.9%, 93.2%, 93.4% MAP，检测速度为35 fps。所提算法与目前效果较好的极小人脸检测方法相比，在保证精度的同时提高了人脸检测速度。Abstract: Face detection is finding and locating all faces in the input image, and then returning the position and size of the faces. It is an important direction of target detection. In order to solve the problem which is caused by the diversity of face size, a new single shot multiscale face algorithm is presented based on feature fusion. This method combines predictions from multiple feature maps with different resolutions to handle faces of various sizes, and the fusion of the feature maps in the shallow layers can improve the detection accuracy of the small size face by introducing the contextual information. Experimental results on the FDDB and WIDERFACE datasets confirm that the proposed method has competitive accuracy. Additionally, the object proposal step is removed, which makes the method fast. The proposed model achieves 87.9%, 93.2% and 93.4% Mean Average Precision (MAP) on the WIDERFACE sub-datasets respectively, at 35 fps. The proposed method outperforms a comparable state-of-the-art HR model, and at the same time improves the speed while ensuring the accuracy.
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表 1 检测框参数
特征层 步长n 检测框大小 宽高比 conv3_3 4 16 1 conv4_3 8 32 1 conv5_3 16 64 1 conv7 32 128 1 conv8_2 64 256 1 conv9_2 128 512 1 
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表 3 实验结果MAP对比
方法 难 适中 简单 检测速度(fps) Faster-rcnn 0.712 0.845 0.897 <10 SSD-face 0.737 0.882 0.910 <43 HR 0.831 0.914 0.925 <5 本文方法 0.879 0.932 0.934 <35
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