多尺度和纹理特征增强的小尺寸人脸检测

Multi-scale and texture feature enhancement for small face detection

针对现有人脸检测算法难以处理多尺度、多姿态的人脸检测,尤其是面对小尺寸时准确性低的问题,提出了多尺度和纹理特征增强的小尺寸人脸检测算法。该算法的多尺度增强模块能够丰富特征的多尺度信息,提高对多尺度人脸的检测能力;纹理特征增强模块能够通过融合低层的纹理信息提升高层语义的表达,从而加强对小尺寸人脸的检测能力;多阶段加权损失函数平衡网络的输出,充分发挥各个模块的增强作用。实验结果表明,该方法不仅在检测速度上可以达到实时,而且对MALF数据集中高度小于60像素的人脸检测精度可达88.69%;在FDDB数据集上相比目前的BBFCN算法精度提高近四个百分点。

In view of the existing face detection algorithms were difficult to deal with multi-scale,multi-pose face detection,especially for small size face,which encountered the problem of low accuracy,this paper proposed a small size face detection algorithm with multi-scale and texture feature enhanced.In this algorithm,the multi-scale enhancement module could enrich the multi-scale features to improve the detection accuracy of multi-scale face,and the texture feature enhancement module could enhance the high-level semantic information by fusing the low-level texture information,thus,it enhanced the detection ability of small size face.Furthermore,the multi-stage weighted loss functions balanced the output of the network and strengthened the role of each module.The experimental results show that the proposed algorithm can not only achieve real-time computation in detection speed,but also achieve 88.69%accuracy for face detection with less than 60 pixels height in MALF dataset.Compared with the BBFCN algorithm in FDDB dataset,the result is increased by nearly 4%.