Joint Sparse Representation for Robust Multimodal Biometrics Recognition

Traditional biometric recognition systems rely on a single biometric signature for authentication. While the advantage of using multiple sources of information for establishing the identity has been widely recognized, computational models for multimodal biometrics recognition have only recently rece...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE transactions on pattern analysis and machine intelligence Vol. 36; no. 1; pp. 113 - 126
Main Authors:	Shekhar, Sumit, Patel, Vishal M., Nasrabadi, Nasser M., Chellappa, Rama
Format:	Journal Article
Language:	English
Published:	Los Alamitos, CA IEEE 01.01.2014 IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Applied sciences Biometric Identification - methods Biometrics Biometrics (access control) Classification algorithms Computer science; control theory; systems Computer systems and distributed systems. User interface Databases, Factual Dermatoglyphics - classification Detection, estimation, filtering, equalization, prediction Exact sciences and technology Face - anatomy & histology feature fusion Humans Information, signal and communications theory Iris - anatomy & histology Joints Kernel Memory and file management (including protection and security) Memory organisation. Data processing Multimodal biometrics Optimization Robustness Signal and communications theory Signal, noise Software Sparse matrices sparse representation Telecommunications and information theory Biometrics Modeling Dimension reduction feature fusion Information source User interface Multiplicity Quality control Selection criterion Data fusion Sparse representation Multimodal interface Computer security Multimodal biometrics Mathematical programming
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!