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.
Highlights
Image matching is an important task in computer vision and multimedia analysis, and numerous methods have been proposed to solve this issue under controlled conditions [1,2,3,4]
Some invalid information is introduced into the reconstructed image, and sometimes the information is even interference for image matching. erefore, to avoid invalid information interference in image reconstruction, some researchers proposed the image matching idea based on coupled metric learning
The same person’s face images have large illumination and pose changes; this intraclass multimodal problem will affect the metric learning and final classification results. e methods in [9, 14] are distance metric learning only based on category label information, which cannot overcome the intraclass multimodal problem, so the recognition rate is lower. e distance metric methods in [11, 13] are based on locality preserving relation in the same class, which are advantageous to solve the intraclass multimodal problem, so comparing with CML and KCML, the recognition effect is improved. e CMDM [15] combines discriminant information with data distribution of local neighbors, so the recognition effect is similar to the proposed Linear Multisupervised Coupled Metric Learning (LMS-CML) method
Summary
Image matching is an important task in computer vision and multimedia analysis, and numerous methods have been proposed to solve this issue under controlled conditions [1,2,3,4]. Erefore, to avoid invalid information interference in image reconstruction, some researchers proposed the image matching idea based on coupled metric learning. E deep coupled metric method maps samples to a coupled space by using deep networks, which can better extract nonlinear features and improve the performance of image matching. Erefore, to better use the spatial distribution information of samples to supervise the metric learning, this paper proposes a multisupervised coupled metric learning method fusing category label and distribution information of samples. E experiments on Yale-B, ORL, and UMIST face datasets demonstrate that the multisupervised coupled metric extends the distance metric methods and effectively improves the image matching performance. (1) We propose a multisupervised coupled metric learning method fusing category label and distribution relationship, which can overcome the defect of existing coupled metric methods.
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