Similarity Metric Learning for Face Recognition

被引：137

作者：

Cao, Qiong ^{[1
]}

Ying, Yiming ^{[1
]}

Li, Peng ^{[2
]}

机构：

[1] Univ Exeter, Dept Comp Sci, Exeter EX4 4QJ, Devon, England

[2] Univ Bristol, Dept Engn Math, Bristol BS8 1TH, Avon, England

来源：

2013 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV) | 2013年

基金：

英国工程与自然科学研究理事会;

关键词：

SCALE;

D O I：

10.1109/ICCV.2013.299

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Recently, there is a considerable amount of efforts devoted to the problem of unconstrained face verification, where the task is to predict whether pairs of images are from the same person or not. This problem is challenging and difficult due to the large variations in face images. In this paper, we develop a novel regularization framework to learn similarity metrics for unconstrained face verification. We formulate its objective function by incorporating the robustness to the large intra-personal variations and the discriminative power of novel similarity metrics. In addition, our formulation is a convex optimization problem which guarantees the existence of its global solution. Experiments show that our proposed method achieves the state-of-the-art results on the challenging Labeled Faces in the Wild (LFW) database [10].

引用

页码：2408 / 2415

页数：8