Multi-View Cosine Similarity Learning with Application to Face Verification

Zining Wang, Jiawei Chen and Junlin Hu
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Zining Wang: School of Software, Beihang University, Beijing 100191, China
Jiawei Chen: College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
Junlin Hu: School of Software, Beihang University, Beijing 100191, China

Mathematics, 2022, vol. 10, issue 11, 1-13

Abstract: An instance can be easily depicted from different views in pattern recognition, and it is desirable to exploit the information of these views to complement each other. However, most of the metric learning or similarity learning methods are developed for single-view feature representation over the past two decades, which is not suitable for dealing with multi-view data directly. In this paper, we propose a multi-view cosine similarity learning (MVCSL) approach to efficiently utilize multi-view data and apply it for face verification. The proposed MVCSL method is able to leverage both the common information of multi-view data and the private information of each view, which jointly learns a cosine similarity for each view in the transformed subspace and integrates the cosine similarities of all the views in a unified framework. Specifically, MVCSL employs the constraints that the joint cosine similarity of positive pairs is greater than that of negative pairs. Experiments on fine-grained face verification and kinship verification tasks demonstrate the superiority of our MVCSL approach.

Keywords: similarity learning; metric learning; multi-view; cosine similarity; face verification (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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