A hybrid classical techniques and optimal decision model for iris recognition under variable image quality conditions

Biometric authentication is one of the most quickly increasing innovations in today's world; this promising technology has seen widespread use in a variety of fields, including surveillance services, safe financial transfers, credit-card authentication. in biometric verification processes such as gender, age, ethnicity is iris recognition technology is considered the most accurate compared to other vital features such as face, hand geometry, and fingerprints. Because the irises in the same person are not similar. In this work, the study of gender classification using Artificial Neural Networks (ANN) based on iris recognition. The eye image data were collected from the IIT Delhi IRIS Database. All datasets of images were processed using various image processing techniques using the neural network. The results obtained showed high performance in training and got good results in testing. ANN's training and testing process gave a maximum performance at 96.4% and 97% respectively.

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A hybrid classical techniques and optimal decision model for iris recognition under variable image quality conditions

Cited by 3 publications

References 53 publications

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An Optimal Decision Model for Electricity Markets Considering Load Characteristics and Electricity Demand

An iris quality evaluation method with pre-recognition screening function

Gender Classification Based on Iris Recognition Using Artificial Neural Networks

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