Thursday, October 3, 2013

IRIS RECOGNITION USING MATLAB







IRIS RECOGNITION USING MATLAB

Introduction
Iris recognition is a method of biometric authentication that uses pattern recognition techniques based on high-resolution images of the irises of an individual's eyes. Not to be confused with another less prevalent ocular-based technology, retina scanning, iris recognition uses camera technology, and subtle IR illumination to reduce specular reflection from the convex cornea to create images of the detail-rich, intricate structures of the iris. These unique structures converted into digital templates, provide mathematical representations of the iris that yield unambiguous positive identification of an individual.
Iris recognition efficacy is rarely impeded by glasses or contact lenses. Iris technology has the smallest outlier (those who cannot use/enroll) group of all biometric technologies. The only biometric authentication technology designed for use in a one-to many search environment, a key advantage of iris recognition is its stability, or template longevity as, barring trauma, a single enrolment can last a lifetime.
Breakthrough work to create the iris recognition algorithms required for image acquisition and one-to-many matching was pioneered by John G.Daugman, who holds key patents on the method. Daugman's algorithms are the basis of almost all currently (as of 2006) commercially deployed iris-recognition systems. It has a so far unmatched practical false-accept rate of zero; that is there is no known pair of images of two different irises that the Daugman algorithm in its deployed configuration mistakenly identifies as the same.
            We will use IRIS authentication technique to control the hardware in our project. The hardware can either be a electro-mechanical lock, generator, access panel, ATM, etc.



IRIS AUTHENTICATION STEPS:
  • Grayscale Conversion
  • NTSC Weighted Averaging Conversion
  • RGB Averaging Conversion
  • Segmentation
  • Sharpening
  • Threshold
  • Edge Detection (Horizontal, Vertical)

NORMALIZATION

DAUGMAN’S RUBBER SHEET MODEL

MATLAB Project List:
A Two-Level FH-CDMA Scheme for Wireless Communication Systems over Fading Channels.
Efficient SNR Estimation in OFDM System.
IMAGE Resolution Enhancement by Using Discrete and Stationary Wavelet Decomposition.
A Fast Adaptive Kalman Filtering Algorithm for Speech Enhancement.
Quality Assessment of Deblocked Images.
Number Plate Recognition for Use in Different Countries Using an Improved Segmentation.
A NOVEL APPROACH OF IMAGE FUSION ON MR AND CT IMAGES USING WAVELET TRANSFORMS.
Stationary and Non-Stationary noise removal from Cardiac Signals using a Constrained Stability Least Mean Square Algorithm.
A New ZCT Precoded OFDM System with Pulse Shaping: PAPR Analysis.
Candidate Architecture for MIMO LTE-Advanced Receivers with Multiple Channels Capabilities and Reduced Complexity and Cost.
Super-Resolution Method for Face Recognition Using Nonlinear Mappings on Coherent Features.
A Single Image Enhancement using Inter-channel Correlation.
Adaptive Steganalysis of Least Significant Bit Replacement in Grayscale Natural Images.
HAIRIS: A Method for Automatic Image Registration Through Histogram-Based Image Segmentation.
Non-blind watermarking scheme for color images in RGB space using DWT-SVD.
Research and implementation of information hiding based on RSA and HVS.
Audio Forensic marking using Quantization in DWT-SVD Domain.
Removal of High Density Salt and Pepper Noise Through Modified Decision Based Unsymmetrical Trimmed Median Filter.
Color Constancy for Multiple Light Sources.
Change Detection in Synthetic Aperture Radar Images based on Image Fusion and Fuzzy Clustering.
On Performance Improvement of Wireless Push Systems via Smart Antennas.
A Semi supervised Segmentation Model for Collections of Images.
Secure Communication in the Low-SNR Regime.
Interpolation-Based Image Super-Resolution Using Multi surface Fitting.

Illumination Recovery from Image with Cast Shadows via Sparse Representation.


FEATURE EXTRACTION
Feature Matching
IRIS Template Bit Pattern Generation (Please note that we wont be using Gabor Filters for this but rather histogram filters)


Bit Pattern Comparison

Result

No comments:

Post a Comment