Papers
Topics
Authors
Recent
Detailed Answer
Quick Answer
Concise responses based on abstracts only
Detailed Answer
Well-researched responses based on abstracts and relevant paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses
Gemini 2.5 Flash
Gemini 2.5 Flash 65 tok/s
Gemini 2.5 Pro 47 tok/s Pro
GPT-5 Medium 39 tok/s Pro
GPT-5 High 32 tok/s Pro
GPT-4o 97 tok/s Pro
Kimi K2 164 tok/s Pro
GPT OSS 120B 466 tok/s Pro
Claude Sonnet 4 38 tok/s Pro
2000 character limit reached

A New IRIS Normalization Process For Recognition System With Cryptographic Techniques (1111.5135v1)

Published 22 Nov 2011 in cs.CV and cs.CR

Abstract: Biometric technologies are the foundation of personal identification systems. It provides an identification based on a unique feature possessed by the individual. This paper provides a walkthrough for image acquisition, segmentation, normalization, feature extraction and matching based on the Human Iris imaging. A Canny Edge Detection scheme and a Circular Hough Transform, is used to detect the iris boundaries in the eye's digital image. The extracted IRIS region was normalized by using Image Registration technique. A phase correlation base method is used for this iris image registration purpose. The features of the iris region is encoded by convolving the normalized iris region with 2D Gabor filter. Hamming distance measurement is used to compare the quantized vectors and authenticate the users. To improve the security, Reed-Solomon technique is employed directly to encrypt and decrypt the data. Experimental results show that our system is quite effective and provides encouraging performance. Keywords: Biometric, Iris Recognition, Phase correlation, cryptography, Reed-Solomon

Citations (24)
List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

Summary

We haven't generated a summary for this paper yet.

Dice Question Streamline Icon: https://streamlinehq.com

Follow-Up Questions

We haven't generated follow-up questions for this paper yet.