基于局部特征和深度神经网络的人脸性别判别模型研究

摘要

深度学习方法可以自动发现更佳数据以改善分类器性能.然而,在计算机视觉任务中,比如性别识别问题,有时候很难直接从整个图像进行学习.因此,提出一种新的基于局部特征和深度神经网络的人脸性别识别模型.首先,该模型从输入图像中提取数个局部特征,并将这些特征反馈给判别图像的深度神经网络,然后根据图像所属标签将每个局部特征分类.最后,使用简单的投票方案对整体图像进行判决.在FERET和CAS-PEAL-R1两个人脸图像资料库上进行了人脸性别分类实验,结果显示提出的方法优于其他深度学习方法,具有较好的准确性和稳定性.%Depth learning method can automatically find better data to improve the performance of classifier.However,in computer vision tasks,such as gender recognition,it is difficult to learn directly from the entire image sometimes.Therefore,a new face recognition model based on local feature and depth neural network is proposed in this paper.Firstly,the model extracts several local features from the input image,and then these features are fed back to the depth neural network of the image,and then each local feature is classified according to the tag.Finally,a simple voting scheme is used to decide the whole image.The experiments of face gender classification are carried out on two face databases of FERET and CAS-PEAL-R1,and the results show that the proposed method is superior to other depth learning methods,and has better accuracy and stability.