Thursday, 23 April 2020

MATLAB code of Palm print Recognition using Gabor filter

Palm print is becoming most essential and relatively large biometric identifier as compared to other biometric traits such as fingerprint, iris, face, retina, signature, and DNA. Palm print identification has various advantages 
(1) low-resolution imaging 
(2) stable line feature 
(3) high user acceptance. 
Earlier techniques like fingerprint identification and iris and retina reorganization require high accuracy but there are some problems with these prior technologies. In palm print feature extraction considered geometrical features are wrinkles, ridges and principle lines singular point, textures, etc. Palm print authentication can be done in two way i.e. Online and offline. 

The matlab implementation of the project is shown in the following YouTube Video. Just have a look.  

Palm print identification system consists of five models, (1) palm print acquisition (2) pre-processing (3) feature extraction (4) matching (5) storage. The five modules are described below: 
1) Palm print Acquisition: A palm print image is captured by our palm print scanner and then the AC signal is converted into a digital signal, which is transmitted to a computer for further processing. 
2) Pre-processing: A coordinate system is set up on the basis of the boundaries of fingers so as to extract a central part of a palm print for feature extraction. 
3) Textured Feature Extraction: We apply a 2-D Gabor filter to extract textural information from the central part. 
4) Matching: A distance measure is used to measure the similarity of two palm prints. 
5) Database: It is used to store the templates obtained from the enrolment phase. Palm print authentication operates in two modes, (1) enrolment and (2) verification.

MATLAB Implementation of the Project:  
Blank GUI of the Project

Palm Print recognition MATLAB GUI - Palm Print Authorized

Palm Print recognition MATLAB GUI - Palm Print unauthorized

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