基于Mask R-CNN的人脸检测与分割方法

针对现有主流的人脸检测算法不具备像素级分割,从而存在人脸特征具有噪声及检测精度不理想的问题,提出了一种基于Mask R-CNN的人脸检测及分割方法。通过ResNet-101结合RPN网络生成候选区域,再利用RoIAlign算法实现像素级的特征点定位,旨在提高定位精度。根据全卷积网络生成相应的人脸二值掩码,实现图像中人脸信息与背景的分割。此外,构建了一个具有分割标注信息的人脸数据集用于训练相应模型。在通用人脸检测数据集的实验结果表明,该方法具有较好的人脸检测效果,并能在准确检测的同时实现像素级的人脸信息分割。

Face Detection and Segmentation Method Based on Mask R-CNN

Face detection is an important research direction in computer vision and information security,which has been widely studied over the past few decades.In the traditional face detection method,there is no pixel-level segmentation process,which leads to the problem of face features with noise and unsatisfactory detection accuracy.In order to overcome this shortcoming,a face detection and segmentation method based on Mask R-CNN is proposed in this paper.In this method,ResNet-101 and RPN is used to generate RoIs,and RoIAlign faithfully retains the exact spatial locations to generate binary mask through Fully Convolution Network.In order to train the model,this paper constructs a face dataset with segmentation annotation information.The experimental results of well-known face detection dataset show that the proposed method has better face detection effect and can achieve pixel-level face information segmentation at the same time.