Performance Evaluation of ANN based Hand Geometry Recognition

Abstract

The research paper includes development of Application GUI for the ANN Hand Geometry based Recognition System with initial stages of Image Acquisition, Image Pre-processing and Feature Extraction and ANN Recognition using MATLAB. The application is to be tested on database for accuracy and performance and analytical comparisons are to be made on basis of testing. The research presents a method based on moment invariant method and Artificial Neural Network (ANN) which uses a four-step process: separates the hand image from its background, normalizes and digitizes the image, applies statistical features like Length and Width of the Fingers, Diameter of the Palm Perimeter Measurements, maxima and mini points and finally implements recognition and was successful in the verification as ANN was trained for seven neural net layers with 150000 iterations each. Neural network with MLP is highly efficient. The ANN is trained and tested on a total of 150 input palm images from CASIA Multi-Spectral Palmprint Image Database. The two different datasets are created for Left Palm Images and Right Palm Images. The Dataset1 includes 90 left palm images from 15 subjects with 06 images from each subject. The Dataset2 includes 60 right palm images from 10 subjects with 06 images from each subject.