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Iris recognition technique is now regarded among the most trustworthy biometrics tactics. This is basically ascribed to its extraordinary consistency in identifying individuals. Moreover, this technique is highly efficient because of iris’ distinctive characteristics and due to its ability to protect the iris against environmental and aging effects...
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... MLP construction for this problem is shown in Figure 4. The XOR function was chosen for this example because it is not extensive and also contains local minima, which causes problems to many other methods. ...
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Citations
... In the iris technology, Nseaf et al. [92] suggest for iris recognition two DNN models from video data. These DNN models include the Bi-propagation and the Stacked Sparse Auto Encoders (SSAE). ...
Deep learning is an evolutionary advancement in the field of machine learning. The technique has been adopted in several areas where the computer after processing volumes of data are expected to make intelligent decisions. An important field of application for deep learning is the area of biometrics wherein the patterns within the uniquely human traits are recognized. Recently, many systems and applications applied deep learning for biometric systems. The deep network is trained on the vast range of patterns, and once the network has learnt all the unique features from the data set, it can be used to recognize similar patterns. Biometric technology that is being widely used by security applications includes recognition based on face, fingerprint, iris, ear, palm-print, voice and gait. This paper provides an overview of some systems and applications that applied deep learning for biometric systems and classifying them according to biometrics modalities. Moreover, we are reviewing the existing system and performance indicators. After a detailed analysis of several existing approaches that combine biometric system with deep learning methods, we draw our conclusion.
