Human iris recognition in post-mortem subjects: Study and database

Human iris is a good subject of biometrical identification, since iris patterns are unique like fingerprints. Iris is well protected against damage, unlike fingerprints, which can be harder to recognize after years of certain types of manual labor.A problem of iris recognition is considered in the paper. In machine learning, pattern recognition is the assignment of a label to a given input value. Pattern classification is an example of pattern recognition: it attempts to assign each input value to one of a given set of classes. Nowadays various techniques are used for this purpose, and in particular artificial neural networks.For iris recognition problem solving Kohenen Self Organizing Maps are suggested to use. The software for iris recognition is developed which is customizable and allows to select the appropriate parameters of the neural network to obtain the most satisfactory results. The developed Self-Organizing Map Library of classes can be used for various kinds of object classification problem solving as well as for any problems suitable to solve with Self-Organizing Maps.

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Human iris recognition for biometric identification

2007 10th International Conference on Computer and Information Technology ◽

10.1109/iccitechn.2007.4579354 ◽

2007 ◽

Cited By ~ 5

Author(s):

Paul Padma Polash ◽

Md. Maruf Monwar

Keyword(s):

Iris Recognition ◽

Biometric Identification ◽

Human Iris

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Performance Analysis of Iris Recognition using Multi Stage Wavelet Transform Decomposition and Bicubic Interpolation Technique

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.e7322.088619 ◽

2019 ◽

Vol 8 (6) ◽

pp. 1-10

Keyword(s):

Wavelet Transform ◽

Discrete Wavelet Transform ◽

Iris Recognition ◽

Image Features ◽

Discrete Wavelet ◽

Multi Stage ◽

Human Iris ◽

The Individual ◽

Matching Score ◽

Bicubic Interpolation

Biometric identification is highly reliable for human identification. Biometric is a field of science used for analyzing the physiological or behavioural characteristics of human. Iris features are unique, stable and can be visible from longer distances. It uses mathematical pattern-recognition techniques on video images of one or both iris of an individual's. Compared to other biometric traits, iris is more challenging and highly secured tool to identify the individual. In this paper iris recognition based on the combination of Discrete Wavelet Transform (DWT), Inverse Discrete Wavelet Transform (IDWT), Independent Component Analysis (ICA) and Binariezed Statistical Image Features (BSIF) are adopted to generate the hybrid iris features. The first level and second level DWT are employed in order to extract the more unique features of the iris images. The concept of bicubic interpolation is applied on high frequency coefficients generated by first level decomposition of DWT to produce new set of sub-bands. The approximation band generated by second level decomposition of DWT and new set of sub-bands produced by second level decomposition of DWT are applied on IDWT to reconstruct the coefficients. The ICA 5x5 filters and BSIF are adopted for selecting the appropriate images to extract the final features. Finally based on the matching score between the database image and test image the genuine and imposters are identified. Using CASIA database, training and testing of the features is performed and performance is evaluated considering different combinations of Person inside Database (PID) and Person outside Database (POD).

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