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Indian Journal of Forensic Medicine & Toxicology
Year : 2019, Volume : 13, Issue : 1
First page : ( 236) Last page : ( 240)
Print ISSN : 0973-9122. Online ISSN : 0973-9130.
Article DOI : 10.5958/0973-9130.2019.00048.3

Hereditary Human Disorders Identification through Finger Print Analysis

Shekan Raid Abd Alreda1,*, Abdulkadium Ahmed Mahdi2, Majid Ali Mohammed Abdul3

1Specialization Computer Science (Information System), College of Education for Pure Science, University of Babylon, Iraq

2Specialization Computer Science (Information System), AL-Qasim Green University, Iraq

3Specialization Computer Science (Information System), Ibn Hayyan University College, Iraq

*Corresponding author: Raid Abd Alreda Shekan. Specialization Computer Science (Information System), College of education for pure science, University of Babylon, Iraq; E-mail: pure.raed.abd@uobabylon.edu.iq.

Online published on 8 February, 2019.

Abstract

In Human Disorders Diabetes and Blood Pressure are predominant. Identification of these disorders can be done using different tools. Finger print analysis can distinguished as the most inexpensive method to distinguish the symptoms of diabetes in the human body. Especially type II diabetes can be identified from the fluctuating asymmetry in fingerprints. Type two Diabetes mellitus is identified as the most dangerous human disorder leads to dysfunction of organs like kidneys, Heart, eyes etc. In biometric identification system finger prints are regarded as the easiest measurable concept from the human body. Finger prints are unique and can be measured with the scanning process. But it is observed that the symptoms of diabetes mellitus causes the fluctuating asymmetry in fingerprints. This variation can be measured and used for identification of diabetes mellitus with the help of biometric scanners with crossmatch verification process. Predominantly the crossmatch analysis process can distinguish the patients with type 2 diabetes mellitus and Type 1 diabetes mellitus. In this paper we focus on the pattern asymmetry as well as wavelet asymmetry readings to identify the symptoms of Type 2 Diabetes Mellitus and Type 1 Diabetes Mellitus.

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Keywords

Finger Print scanning, diabetes mellitus, asymmetry scores, pattern analysis, ridge counts.

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