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. We modify our model so that it is robust to noise and occlusion. A multimodal quality measure is also proposed to weigh each modality as it gets fused. Furthermore, we also kernelize the algorithm to handle non-linearity in data. The optimization problem is solved using an efficient alternative direction method. Various experiments show that our method compares favorably with competing fusion-based methods.

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Data-driven sparse models have been shown to give superior performance for image classification tasks. Most of these works depend on learning a synthesis dictionary and the corresponding sparse code for recognition. However in recent years, an alternate analysis coding based framework (also known as co-sparse model) has been proposed for learning sparse models. In this paper, we study this framework for image classification. We demonstrate that the proposed approach is robust and efficient, while giving a comparable or better recognition performance than the traditional synthesis-based models.Index Termsanalysis sparse coding models, efficient sparse coding, image classification