Abstract
Recently, user identification is an essential foundation for protecting information in several applications. However, the need for heightened this identification has expanded the research to focus on the biometric traits of the users. Traditionally, identity determination is assured by recognizing personals used classical security means such password and/or card. Recently, a natural and reliable solution to this problem is offered by biometrics technologies. So, among several biometric modalities, these extracted from the hand have been systematically used to make identification for last years. In other hand, all issues related to the final conception of a biometric system are generally related to the classification task (classifier used). In this paper, we present a Restricted Boltzmann Machine (RBM) for palmprint (PLP) and palm-vein (PLV) identification which are able to classify precisely these modalities. In addition, in order to improving the identification system accuracy, a RBM based Deep Belief Nets (DBN) is also presented. The obtained results, using databases of 400 persons, have showed that deep learning methods has higher performances compared to the classical methods developed in the literature in terms of systems accuracies.
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Meraoumia, A., Chitroub, S., Chergui, O., Bendjenna, H. (2018). Investments in Deep Learning Techniques for Improving the Biometric System Accuracy. In: Alenezi, M., Qureshi, B. (eds) 5th International Symposium on Data Mining Applications. Advances in Intelligent Systems and Computing, vol 753. Springer, Cham. https://doi.org/10.1007/978-3-319-78753-4_15
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DOI: https://doi.org/10.1007/978-3-319-78753-4_15
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