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...

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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
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Abstract	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 received attention. We propose a multimodal sparse representation method, which represents the test data by a sparse linear combination of training data, while constraining the observations from different modalities of the test subject to share their sparse representations. Thus, we simultaneously take into account correlations as well as coupling information among biometric modalities. A multimodal quality measure is also proposed to weigh each modality as it gets fused. Furthermore, we also kernelize the algorithm to handle nonlinearity in data. The optimization problem is solved using an efficient alternative direction method. Various experiments show that the proposed method compares favorably with competing fusion-based methods.
AbstractList	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 received attention. We propose a multimodal sparse representation method, which represents the test data by a sparse linear combination of training data, while constraining the observations from different modalities of the test subject to share their sparse representations. Thus, we simultaneously take into account correlations as well as coupling information among biometric modalities. A multimodal quality measure is also proposed to weigh each modality as it gets fused. Furthermore, we also kernelize the algorithm to handle nonlinearity in data. The optimization problem is solved using an efficient alternative direction method. Various experiments show that the proposed method compares favorably with competing fusion-based methods. 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 received attention. We propose a multimodal sparse representation method, which represents the test data by a sparse linear combination of training data, while constraining the observations from different modalities of the test subject to share their sparse representations. Thus, we simultaneously take into account correlations as well as coupling information among biometric modalities. A multimodal quality measure is also proposed to weigh each modality as it gets fused. Furthermore, we also kernelize the algorithm to handle nonlinearity in data. The optimization problem is solved using an efficient alternative direction method. Various experiments show that the proposed method compares favorably with competing fusion-based methods.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 received attention. We propose a multimodal sparse representation method, which represents the test data by a sparse linear combination of training data, while constraining the observations from different modalities of the test subject to share their sparse representations. Thus, we simultaneously take into account correlations as well as coupling information among biometric modalities. A multimodal quality measure is also proposed to weigh each modality as it gets fused. Furthermore, we also kernelize the algorithm to handle nonlinearity in data. The optimization problem is solved using an efficient alternative direction method. Various experiments show that the proposed method compares favorably with competing fusion-based methods.
Author	Nasrabadi, Nasser M. Shekhar, Sumit Patel, Vishal M. Chellappa, Rama
Author_xml	– sequence: 1 givenname: Sumit surname: Shekhar fullname: Shekhar, Sumit email: sshekha@umiacs.umd.edu organization: Dept. of Electr. & Comput. Eng., Univ. of Maryland, College Park, MD, USA – sequence: 2 givenname: Vishal M. surname: Patel fullname: Patel, Vishal M. email: pvishalm@umiacs.umd.edu organization: Dept. of Electr. & Comput. Eng., Univ. of Maryland, College Park, MD, USA – sequence: 3 givenname: Nasser M. surname: Nasrabadi fullname: Nasrabadi, Nasser M. email: nasser.m.nasrabadi@us.army.mil organization: U.S. Army Res. Lab., Adelphi, MD, USA – sequence: 4 givenname: Rama surname: Chellappa fullname: Chellappa, Rama email: rama@umiacs.umd.edu organization: Dept. of Electr. & Comput. Eng., Univ. of Maryland, College Park, MD, USA
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Keywords	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
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Snippet	Traditional biometric recognition systems rely on a single biometric signature for authentication. While the advantage of using multiple sources of information...
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SubjectTerms	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
Title	Joint Sparse Representation for Robust Multimodal Biometrics Recognition
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