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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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence 2014-01, Vol.36 (1), p.113-126
Main Authors: Shekhar, Sumit, Patel, Vishal M., Nasrabadi, Nasser M., Chellappa, Rama
Format: Article
Language:English
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
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Summary: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.
ISSN:0162-8828
1939-3539
2160-9292
DOI:10.1109/TPAMI.2013.109