A Novel Multisupervised Coupled Metric Learning for Low-Resolution Face Matching. (16th March 2020)

• Record Type: Journal Article
• Authors: Zou, Guofeng; Fu, Guixia; Peng, Xiang
• Other Names: Kotropoulos Constantine Academic Editor.
• Is Part Of: Advances in multimedia. Volume 2020(2020)
• Publication Date: 2020-03-16
• Subjects: Multimedia systems -- Periodicals; Computer networks -- Periodicals; Multimédia; Réseaux d'ordinateurs; Computer networks; Multimedia systems; Periodicals; 006.7
• DOI: 10.1155/2020/3197623
• Languages: English
• ISSNs: 1687-5680

Abstract

Abstract : This paper presents a new multisupervised coupled metric learning (MS-CML) method for low-resolution face image matching. While coupled metric learning has achieved good performance in degraded face recognition, most existing coupled metric learning methods only adopt the category label as supervision, which easily leads to changes in the distribution of samples in the coupled space. And the accuracy of degraded image matching is seriously influenced by these changes. To address this problem, we propose an MS-CML method to train the linear and nonlinear metric model, respectively, which can project the different resolution face pairs into the same latent feature space, under which the distance of each positive pair is reduced and that of each negative pair is enlarged. In this work, we defined a novel multisupervised objective function, which consists of a main objective function and an auxiliary objective function. The supervised information of the main objective function is the category label, which plays a major supervisory role. The supervised information of the auxiliary objective function is the distribution relationship of the samples, which plays an auxiliary supervisory role. Under the supervision of category label and distribution information, the learned model can better deal with the intraclass multimodal problem, and the features obtained in the coupled space are more easily matched correctly. Experimental results on three different face datasets validate the efficacy of the proposed method.