Joint Sparse Representation for Robust Multimodal Biometrics Recognition

• 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
• DOI: 10.1109/TPAMI.2013.109

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.