Coupled Attribute Learning for Heterogeneous Face Recognition
Coupled Attribute Learning for Heterogeneous Face Recognition
复制标题
异构人脸识别的耦合属性学习
DOI：
10.1109/tnnls.2019.2957285
复制
发表时间：
2020-11-01
影响因子：
10.4
通讯作者：
Peng, Chunlei
中科院分区：
文献类型：
--
作者：
Liu, Decheng;Gao, Xinbo;Peng, Chunlei
异构人脸识别是人脸识别中的一个具有挑战性的问题，人脸图像具有很大的纹理和空间结构差异。与传统的均匀环境下的人脸识别不同，现实中存在着许多来自不同来源（包括不同传感器或不同机制）的人脸图像。此外，有限的训练样本的跨模态对HFR更具有挑战性，由于这些图像的复杂的生成过程。尽管近年来已经取得了很大的进展，现有的工作主要集中在HFR仅从跨模态图像匹配。然而，更实际的是在真实世界的情况下，其中的语义描述线索几乎总是在图像生成过程中获得的面部图像和面部属性的语义描述。受人类认知机制的启发，我们自然地利用显式不变语义描述，即，面部属性，以帮助解决不同模态的面部图像之间的差距。现有的人脸属性相关人脸识别方法主要将属性作为提高识别性能的高级特征，忽略了人脸属性与身份之间的内在联系。在这篇文章中，我们提出了新的耦合属性学习的HFR（CAL-HFR）方法，而无需手动标记的属性。深度卷积网络用于将异构场景中的人脸图像直接映射到紧凑的公共空间，其中距离被视为对的相异性。耦合属性引导三重丢失（CAGTL）被设计用于训练端到端HFR网络，该网络可以有效地消除错误估计属性的缺陷。在多个异构场景上的实验表明，与现有方法相比，该方法具有上级性能.此外，我们公开提供我们生成的成对注释的异构面部属性数据库的评估和促进相关研究。
Heterogeneous face recognition (HFR) is a challenging problem in face recognition and subject to large textural and spatial structure differences of face images. Different from conventional face recognition in homogeneous environments, there exist many face images taken from different sources (including different sensors or different mechanisms) in reality. In addition, limited training samples of cross-modality pairs make HFR more challenging due to the complex generation procedure of these images. Despite the great progress that has been achieved in recent years, existing works mainly focus on HFR from only cross-modality image matching. However, it is more practical to obtain both facial images and semantic descriptions about facial attributes in real-world situations, in which the semantic description clues are nearly always obtained during the process of image generation. Motivated by human cognitive mechanisms, we naturally utilize the explicit invariant semantic description, i.e., face attributes, to help address the gap among face images of different modalities. Existing facial attributes-related face recognition methods primarily regard attributes as the high-level features used to enhance recognition performance, ignoring the inherent relationship between face attributes and identities. In this article, we propose novel coupled attribute learning for the HFR (CAL-HFR) method without labeling the attributes manually. Deep convolutional networks are employed to directly map face images in heterogeneous scenarios to a compact common space where distances are taken as dissimilarities of pairs. Coupled attribute guided triplet loss (CAGTL) is designed to train an end-to-end HFR network that can effectively eliminate defects of incorrectly estimated attributes. Extensive experiments on multiple heterogeneous scenarios demonstrate that the proposed method achieves superior performance compared with that of state-of-the-art methods. Furthermore, we make publicly available our generated pairwise annotated heterogeneous facial attribute database for evaluation and promoting related research.