Hand contour classification using evolutionary algorithm

Abstract

A biometric identification of persons wchich utilize contour of a human hand belogs to still very interesting and still not totally explored areas and its accuracy and effectiveness depends on technical capabilities to some extent. Presented paper solves given problem using combination of different algorithms. A hand contour is used, topological description of the hand, evolutionary algorithm, algorithm linear regression to estimate the knuckles positions and for contours comparison is used an algorithm Iterative Closest Point (ICP) in its genuine shape. All 5 fingers is at computer classification fully moveable, thumb has 2 knuckles. Modern evolutionary optimizers enable markedly to cut down computational demands of the algorithm ICP. Experimental verification of proposed recipes were performed with use of two different databases named THID and GPDS with persons of both gender and different age (cca 20-65let) with total number of oeprons in individual database 104 and 94. Experimental results checked succesfuly suitability of use combination of methods ICP and evolutionary optimizer which is named as EPSDE for solving of the given task with algorithmic complexity O(N) and success rate give by coefficient THID:EER=0.38% and GPDS:EER=0.35% on real images.

Evolutionary Image Registration: A Review

“…Many researchers report the results in terms of success rate. In some cases, the success rate is defined as the recognition capability, the algorithms proposed in [ 57 , 66 , 67 , 68 , 69 , 70 , 71 , 78 , 105 , 112 ] achieving a maximum rate of 100%. Some articles analyze the robustness of the algorithms against additive/blurry/motion noise, the success rate reaching 96% in [ 59 ] (48 of 50 pairs correctly matched) and 98.8% in [ 87 ].…”

“…Moravec, 2020: In [ 105 ], an evolutionary algorithm is applied to the problem of the biometric identification of people. The proposed algorithm, called eaICP, matches the contour of a scanned hand with the contour of a hand stored in database for identification.…”