计算机科学 ›› 2019, Vol. 46 ›› Issue (11A): 246-250.
葛宏孔, 罗恒利, 董佳媛
GE Hong-kong, LUO Heng-li, DONG Jia-yuan
摘要: 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。
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