Abstract
Nowadays, Iris recognition is a method of biometric verification of the person authentication process based on the human iris unique pattern, which is applied to control system for high level security. It is a popular system for recognizing humans and essential to understand it. The objective of this method is to assign a unique subject for each iris image for authentication of the person and provide an effective feature representation of the iris recognition with the image analysis. This paper proposed a new optimization and recognition process of iris features selection by using proposed Modified ADMM and Deep Learning Algorithm (MADLA). For improving the performance of the security with feature extraction, the proposed algorithm is designed and used to extract the strong features identification of iris of the person with less time, better accuracy, improving performance in access control and in security level. The evaluations of iris data are demonstrated the improvement of the recognition accuracy. In this proposed methodology, the recognition of the iris features has been improved and it incorporates into the iris recognition systems.
Highlights
Recognition of bodily features has been used for the purpose of identification and security such as the face, fingerprint, and iris
In order to overcome the limitation of the existing work, the Modified Alternating Direction Method of Multiplexer (ADMM) and Deep Learning algorithm are proposed for optimizing the extraction of the features for strong features with better performance and accuracy
The input query image will undergo the process of proposed MADMM algorithm which is discussed in training phase and the required portion of image data is preserved in it
Summary
Recognition of bodily features has been used for the purpose of identification and security such as the face, fingerprint, and iris. Umamaheswari recognition of iris model to support biometric experiments It illustrates the iris biometrics matching, where different data are employed for training and testing the enrollment, and often leads to reduced performance. Iris is a ring shaped region with biometric patterns It is stable and provides a reliable approach for individual authentication. The state-of-the-art of the iris recognition methods has a unique image differentiation by its feature extraction strategies. In preprocessing, it eliminated the unwanted parts of the image and controlled the pupil function with adjusting the size of the image. The iris images within the short distance and under constrained environment are obtained using NIR [4] [5].
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