Joint Sparsity-Based Robust Multimodal Biometrics Recognition

  • Sumit Shekhar
  • Vishal M. Patel
  • Nasser M. Nasrabadi
  • Rama Chellappa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7585)


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 novel multimodal multivariate sparse representation method for multimodal biometrics recognition, 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 between biometric modalities. Furthermore, the model is modified to make it robust to noise and occlusion. The resulting optimization problem is solved using an efficient alternative direction method. Experiments on a challenging public dataset show that our method compares favorably with competing fusion-based methods.


Sparse Representation Augmented Lagrangian Method Alternative Direction Method Biometric Trait Feature Level Fusion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Sumit Shekhar
    • 1
  • Vishal M. Patel
    • 1
  • Nasser M. Nasrabadi
    • 2
  • Rama Chellappa
    • 1
  1. 1.University of MarylandCollege ParkUSA
  2. 2.Army Research LabAdelphiUSA

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