NDLI: The Application of Graph Kernels in Face Recognition

Content Provider	IEEE Xplore Digital Library
Author	Qiang-rong Jiang Huan-jun Chen Bo Liu
Copyright Year	2012
Abstract	Face recognition has become one of the hot research topics in pattern recognition and image processing in the recent several years, as a result of the wide application in the areas of security control and human-machine interaction. And it has been recognized as the most simplest and non-intrusive technology without hazardous problems, compared to other biometric recognition technology, such as fingerprint recognition, iris recognition and et al. Many scholars dedicated to do research and propose various methods to improve the accuracy and speed of face recognition. As most real-world data is structured, the kernel method, investigated for various kinds of structured data after the successional propose of statistical learning theory and support vector machine, reduced computation and performances well. One of the most widely used tools for modeling structured data are graph and an interesting and important challenge is thus to investigate kernels on instances that are represented by graphs. But so far, only very specific graphs such as trees and strings have been considered. In this paper, we propose two graph kernels including maximum spanning-tree kernel and cycle kernel based on minimum spanning-tree. The experimental results show that the two graph kernels achieve good performance.
Starting Page	1
Ending Page	7
File Size	694496
Page Count	7
File Format	PDF
ISBN	9781467321808
e-ISBN	9781467321815
e-ISBN	9781467321792
DOI	10.1109/DICTA.2012.6411714
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-12-03
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Databases Face recognition Feature extraction Face Kernel Facial features
Content Type	Text
Resource Type	Article

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