Study of edge detection methods based on palmprint lines

The palm was used in fortune telling 3000 years ago. Thus, During this period, many different problems related to palmprint recognition have been addressed. In the recent years, the palm print has been used for biometric applications as human verification and identification. The palm print has many features comparing with a fingerprint, The palm print has number of lines. One group of these lines is known as the principle lines which contains three lines(head line, heart line and life line). The lines are extracted from palm print image by edge detection algorithm which is implementing on ROI of palm print. The main goal of edge detection algorithm is to produce a line and extract important features and reduce the amount of data in the image. This paper investigates the several edge detection methods such as Sobel, Prewitt, Roberts, LOG, and Canny. In addition, we used edge detection using local entropy information and local variance. The experiment is tested on samples taken from four palm print databases (CASIA, PolyU, IIT and database available online). The analysis work has been performed by using PSNR and MSE of resultant images on these popular edge detection methods which improve the palm print matching process. The Prewitt, Roberts and LOG edge detection methods ignore the small lines and identify only the main longer lines while the Sobel identifies the medium and longer lines. The canny edge detection algorithm identifies the complete set of edges of various sizes. From experiment it was seen that good result found with an online database and polyU database by classical edge detection methods.