基于深度学习的非实验室场景人脸属性识别

Face Attributes in Wild Based on Deep Learning

非实验室场景下的人脸图片数量巨大,更加贴近生活,对其进行识别具有较大的研究价值。文中对非实验室环境下的人脸属性识别问题进行了研究,提出了一种人脸属性识别网络(Regional Multiple Layer Attributes Related Net,RMLARNet),不仅对人脸特征的提取方式进行了研究,还挖掘了人脸属性间的关系。该网络由3个部分组成:1、将人脸图像分割成包含属性部位的多个局部区域,并将这些局部区域作为输入提取特征信息;2、以Inception V3为迁移模型,采取多个不相邻卷积层迁移方式提取人脸特征;3、搭建了一个以人脸属性关系为约束的属性识别网络。实验结果表明,对CelebA数据集进行筛选处理,创建属性样本较平衡的CelebA-数据集,并在该数据集上设计实验将取得优于现有方法的实验效果。

Faces in the wild are huge in number and more close to life,and the recognition of facial attributes is a valuable research.A face attributes recognition method named RMLARNet(Regional Multiple Layer Attributes Related Net)was proposed for faces in the wild,which explores a new feature extraction method and attributes relationship.The processing steps of this method are as follows:1、Feature extraction is based on the regional parts of image.2、Features are extracted from different layer of Inception V3,and they are concatenated to get the final face feature.3、An attributes relationship related network is used for attributes recognition.The experiment is conducted on a balanced CelebA-data set which is a subset of CelebA,and this method outperforms state-of-the art methods.