Cao Bing, Wang Nannan, Li Jie, Gao Xinbo
IEEE Trans Neural Netw Learn Syst. 2019 Jun;30(6):1731-1743. doi: 10.1109/TNNLS.2018.2872675. Epub 2018 Oct 25.
Heterogeneous face recognition (HFR) is the process of matching face images captured from different sources. HFR plays an important role in security scenarios. However, HFR remains a challenging problem due to the considerable discrepancies (i.e., shape, style, and color) between cross-modality images. Conventional HFR methods utilize only the information involved in heterogeneous face images, which is not effective because of the substantial differences between heterogeneous face images. To better address this issue, this paper proposes a data augmentation-based joint learning (DA-JL) approach. The proposed method mutually transforms the cross-modality differences by incorporating synthesized images into the learning process. The aggregated data augments the intraclass scale, which provides more discriminative information. However, this method also reduces the interclass diversity (i.e., discriminative information). We develop the DA-JL model to balance this dilemma. Finally, we obtain the similarity score between heterogeneous face image pairs through the log-likelihood ratio. Extensive experiments on a viewed sketch database, forensic sketch database, near-infrared image database, thermal-infrared image database, low-resolution photo database, and image with occlusion database illustrate that the proposed method achieves superior performance in comparison with the state-of-the-art methods.
异质人脸识别（HFR）是对从不同来源捕获的人脸图像进行匹配的过程。HFR在安全场景中发挥着重要作用。然而，由于跨模态图像之间存在相当大的差异（即形状、风格和颜色），HFR仍然是一个具有挑战性的问题。传统的HFR方法仅利用异质人脸图像中包含的信息，由于异质人脸图像之间存在显著差异，这种方法并不有效。为了更好地解决这个问题，本文提出了一种基于数据增强的联合学习（DA-JL）方法。所提出的方法通过将合成图像纳入学习过程来相互转换跨模态差异。聚合的数据扩大了类内规模，提供了更多的判别信息。然而，这种方法也降低了类间多样性（即判别信息）。我们开发了DA-JL模型来平衡这一困境。最后，我们通过对数似然比获得异质人脸图像对之间的相似度得分。在视图草图数据库、法医草图数据库、近红外图像数据库、热红外图像数据库、低分辨率照片数据库和遮挡图像数据库上进行的大量实验表明，与现有方法相比，所提出的方法具有卓越的性能。
