Iris Recognition Report: Implementation, Analysis and Evaluation

This report provides an in-depth analysis of iris recognition, exploring the implementation and evaluation of various methods. It begins by introducing biometric systems and their applications, focusing specifically on iris recognition. The report then delves into the technical aspects, including image processing techniques, edge detection, and normalization. The study examines the challenges associated with iris recognition, such as noise and occlusion, and discusses the importance of iris radius. Furthermore, the report explores the use of standard camera equipment and the critical role of image quality in the recognition process. Overall, the report offers a comprehensive understanding of iris recognition, providing valuable insights into its practical applications and underlying methodologies.

Recognition
of Iris by using
standard camera

Introduction
 A biometric system allows a person to be automatically identified
based on those specific characteristics or features which they possess
 Biometric systems were developed using fingerprints, facial features,
expression, handwriting, iris, hand geometry and retina
 Biometric architecture captures the particular specimen

Objective
 To implement the iris recognition and identification program
 To understand the fundamental science behind iris detection
 To analyze the usage of the standard IR cameras, identify and rate
the problems that occur while imaging using current iris processing
methods contained

Problem Statement
 Resolution
 Reflection
 Noise
 Occlusion
 Focus

Biometric identification for IR
 Chosen element of the human body between two samples such as
voice, iris and fingerprints
 Suggest significant consistency for the detection
 Substantial dissimilarity for samples from separate individuals
 No discrepancy between the samples

Image processing for IR
 Edge detection
 Orientation in the image
 Canny operator

Methods of Iris recognition
 IR Process
 Segmentation
 Normalization
 Mask Generation
 Encoding and Matching

Conclusion
 This can be concluded by this study that, the recognition of the iris
can be inferred that an IR device can be designed using normal
camera equipment, and the output of such a device will depend on
the image quality obtained.
 For the good recognition output an iris radius of the 45 pixels is
found to be appropriate.

Bibliography
 Bowyer, K.W. and Burge, M.J. eds., 2016. Handbook of iris recognition.
Springer London.
 Hamouchene, I. and Aouat, S., 2014. A new texture analysis approach
for iris recognition. AASRI Procedia, 9, pp.2-7.
 Raghavendra, R., Raja, K.B. and Busch, C., 2015. Exploring the
usefulness of light field cameras for biometrics: An empirical study on
face and iris recognition. IEEE Transactions on Information Forensics
and Security, 11(5), pp.922-936.
 Shah, N. and Shrinath, P., 2014. Iris recognition system–a
review. International Journal of Computer and Information
Technology, 3(02), pp.321-327.