This paper presents a method for on-line Handwriting Signature Verification (HSV) using Extreme Points Matching (EPM). EPM does not use direct computation of curve’s curvature thus it does not expect smoothness in the analyzing process of the trajectory. In the proposed method the curve’s form is described by a small set of extreme points and the method thus seems more precise. Furthermore, it provides an effective preprocessing of the curve and can be utilized in one-to-many pattern matching in a restricted access systems.
The fingerprint is used in many vital applications important to humans, which requires searching for an effective way to extract the characteristics of the fingerprint. In this paper we will study some of the most popular methods used to extract fingerprints features. For each method the efficiency, accuracy, flexibility and sensitivity for image rotation will be experimentally tested, measured, analyzed in order to give good recommendations of how and when to use a certain method of features extraction. A detailed comparison analysis between MLBP, K_means, WPT, Minutiae methods will be done using several color images in various rotation modes to insure the stability of image features.
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