基于Faster R-CNN的人脸检测方法

近年来，基于候选区域的快速卷积神经网络（Faster R-CNN）算法，在多个目标检测数据集上有出色的表现，吸引了广泛的研究兴趣. Faster R-CNN 框架本来是用做通用目标检测的，本文将它应用到人脸检测上，分别使用ZF和VGG16卷积神经网络，在WIDER人脸数据集上训练Faster R-CNN模型，并在FDDB人脸数据库上测试. 实验结果表明，该方法对复杂光照、部分遮挡、人脸姿态变化具有鲁棒性，在非限制性条件下具有出色的人脸检测效果. 这两种网络结构，在检测效率和准确性上各有优势，可以根据实际应用需求，选择使用合适的网络模型.

Face Detection Using the Faster R-CNN Method

Recently, the faster R-CNN has demonstrated impressive performance on various object detection benchmarks, and it has attracted extensive research interests. We train a faster R-CNN model on the WIDER face dataset with the ZF and VGG16 convolutional neural network respectively, and then we test the trained model on the FDDB face benchmark. Experimental results demonstrate that the method is robust to complex illumination, partial occlusions and facial pose variations. It achieves excellent performance in detecting unconstrained faces. The two kinds of network have their own advantages in detection accuracy and efficiency, so we can choose to use an appropriate network model according to the actual application requirements.