Design and Implementation of Intelligent Singer Recognition System

Ge, Can; Li, Juanjuan; Liu, Lina; Li, Gukai; Yang, Guo‐Yu; Tang, Hui; Yang, Hanlin; Xia, Yifan; Bao, Rui; Zhang, Haiyu

doi:10.1088/1757-899x/790/1/012143

Cited by 2 publications

(1 citation statement)

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“…Biometric is classified as physical and behavioural, the behavioural is the signature, voice and handwriting, while the physical consists of DNA, palm, fingerprint etc. [1][2][3]. Generally, a signature is conventionally accepted as a biometric for identification of an individual, it represents some behavioural properties of a person, thus widely accepted in schools, banks, organisations hospitals as a means for verification and identification [4].…”

Section: Introductionmentioning

confidence: 99%

Off-line Handwritten Signature Verification System: Artificial Neural Network Approach

Tahir¹,

Ausat²,

Bature³

et al. 2021

IJISA

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Nowadays, it is evident that signature is commonly used for personal verification, this justifies the necessity for an Automatic Verification System (AVS). Based on the application, verification could either be achieved Offline or Online. An online system uses the signature’s dynamic information; such information is captured at the instant the signature is generated. An offline system, on the other hand, uses an image (the signature is scanned). In this paper, some set of simple shaped geometric features are used in achieving offline Verification of signatures. These features include Baseline Slant Angle (BSA), Aspect Ratio (AR), and Normalized Area (NA), Center of Gravity as well as the line’s Slope that joins the Center of Gravities of the signature’s image two splits. Before the features extraction, a signature preprocessing is necessary to segregate its parts as well as to eliminate any available spurious noise. Primarily, System training is achieved via a signature record which was acquired from personalities whose signatures had to be validated through the system. An average signature is acquired for each subject as a result of incorporating the aforementioned features which were derived from a sample set of the subject’s true signatures. Therefore, a signature functions as the prototype for authentication against a requested test signature. The similarity measure within the feature space between the two signatures is determined by Euclidian distance. If the Euclidian distance is lower than a set threshold (i.e. analogous to the minimum acceptable degree of similarity), the test signature is certified as that of the claiming subject otherwise detected as a forgery. Details on the stated features, pre-processing, implementation, and the results are presented in this work.

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