Personal Authentication Based on Curvelet Transform of Palm Print Moments

Abstract

Image transformation provide deep meaning about images feature, so many type of image transformation are appear in the last decade years, one of them is curvelet transformation which improve the image processing techniques specially in field of feature extraction. Personal authentication adopt biometric information to be one of the major coefficients in this field.
 Palm print one of the main approaches for personal identification. So studying the moments extracted from coefficients of curvelet transform of palm print image adopted in order to get high efficient system for personalization systems. Two major phases are constructed in this research to adopt the moments of low frequency coefficient of the curvelet for personal identification. In the first phase a database was built for 50 persons by acquisition nine images for both hands (9 for left hand and 9for right hand). images are acquired and then processed to extract ROI (region of interest) by looking for the palm centroid then a square shape will be fixed based on that centroid. This preprocess play an important step for stable features. Histogram is applied to the images and then apply SOBLE operator and morphological operation to highlight features of palm print, then apply decomposition on each image based on curvelet transformation. Select low frequency coefficient (which hold the details). Evaluation of seven moments for each image (18images) then store there in the database file (so each person will have 126 values), this phase called personal database preparation. While the second phase is the detection phase, which apply the same steps to evaluate the moments as done in first phase then go through the database looking for the closest person to the tested one.
 System evaluation measured by statistical metrics which show good result goes up to 96% when applied on 50 person with different acquisition conditions. Also the effect of ROI dimension with individual hands and integrated both of them studied, which yield to recommended dimension of 192*192.