An enhanced thermal face recognition method based on multiscale complex fusion for Gabor coefficients

The recognition of thermal face is a very promising strategy in biometrics. It is invariant to illumination, robust to pose and immune to forgery. However, thermal face image consist of face heat energy and face counter information mainly, and it makes lower discrimination for inter-class. In this p...

Full description

Saved in:
Bibliographic Details
Published in:Multimedia tools and applications 2014-10, Vol.72 (3), p.2339-2358
Main Authors: Wang, Ning, Li, Qiong, Abd El-Latif, Ahmed A., Peng, Jialiang, Niu, Xiamu
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Artificial intelligence
Biometrics
Blocking
Computer Communication Networks
Computer Science
Computer science
control theory
systems
Computer systems and distributed systems. User interface
Data Structures and Information Theory
Discriminant analysis
Discrimination
Energy
Exact sciences and technology
Face
Face recognition
Image processing systems
Invariants
Multimedia
Multimedia communications
Multimedia Information Systems
Pattern recognition. Digital image processing. Computational geometry
Principal components analysis
Software
Special Purpose and Application-Based Systems
Standard deviation
Strategy
Studies
Thermal energy
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The recognition of thermal face is a very promising strategy in biometrics. It is invariant to illumination, robust to pose and immune to forgery. However, thermal face image consist of face heat energy and face counter information mainly, and it makes lower discrimination for inter-class. In this paper, an enhanced thermal face recognition approach based on Multiscale Complex Fusion for Gabor coefficients (MCFG) is proposed. Initially, the Complex Gabor Jet Descriptor (CGJD) is acquired based on the block mean and standard deviation generated from the magnitude, phase, real and imaginary parts of Gabor coefficients. Then, the Complex LDA (CLDA) algorithm and feature level fusion are implemented on multiscale Gabor coefficients to reduce the dimension and enhance the discrimination. Experiments conducted on two thermal face databases NVIE and IRIS, which have some challenging thermal face images, show that the proposed thermal face recognition approach significantly outperforms the state-of-the-art approaches.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-013-1551-4