Open AccessHand Print Recognition System based on FP-Growth Algorithm
Author(s) -
Haitham Salman Chyad,
Raniah Ali Mustafa,
Kawther Thabt Saleh
Publication year - 2022
Publication title -
webology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.259
H-Index - 18
ISSN - 1735-188X
DOI - 10.14704/web/v19i1/web19067
Subject(s) - artificial intelligence , computer science , hsl and hsv , computer vision , grayscale , canny edge detector , color space , rgb color model , edge detection , segmentation , rgb color space , pattern recognition (psychology) , color image , image processing , image (mathematics) , virus , virology , biology
Hand print recognition system received great interest in the recent few years such as human-computer interaction, computer vision, and computer graphics. In this paper, proposed system for recognition human handprint based on FP-growth algorithm, the system consists of three-stage. The first stage the detection algorithm using HSV color space, canny algorithm and contrast enhancement for grayscale. In this stage separate skin area in-handprint image through first HSV color space converting RGB to HSV color space as well as conducting specific rules for determining the skin area. And then applies skin hand segmentation for the split of non skin and skin areas where hand skin color detection. After the hand detection stage, the first stage in edge detection is image smoothing through using a Gaussian filter then converted to a grayscale image after then contrast enhancement is an important step in the algorithm detection hand. Finally applying canny edge detection. The second stage extract features through apply seven moment invariants. The three-stage applying FP-growth algorithm for recognition handprint image. The system which has been proposed utilize handprint images databases, the database proposed a large data-set of human hand images, 11K Hands, that consists of palmar and dorsal sides of the human hand images dataset that collective database from 190 various subject’s handprint images is made publicly obtainable. The handprint recognition system achieved rate of 92.70%.