Examining Various Biometric Technology in Distinguishing Monozygotic Twins

Abstract

Biometrics is one of the major trending technologies which is used vastly in detecting a person’s unique physical characteristics. Due to myriad objective biometric technology has seen a constant mounting progress. Biometrics serves effective, in identification and verification of biological features in an individual. Subsequently, it functions well in case of illegal happenings. Recognizing a person’s unique characteristics by means of biometrics seems to be extremely complicated in case of twins, specifically in analyzing monozygotic twins who are a replica of each other. A study says that as monozygotic twins are always of the same sex as they are developed from a single egg. Therefore the difficulty in differentiating identical or monozygotic twins should be resolved. Differentiation in a person can be done by either considering single biometric feature or multiple biometric features. Multiple biometric feature recognition provides high accuracy and hence proves to be more efficient when compared with single biometric feature recognition. Human’s finger print and face authentications are considered as distinctive a feature which is practiced immensely in the present days for characterization. Methods such as soft clustering, soft K means clustering, Cartesian distance methods, Principle Component analysis, GLCM Dis-Mean Algorithm etc. are some algorithms used in detecting the biometric traits in twins. In this paper we study how to diversify monozygotic twins by comparing their biometric features such as facial features, facial marks, hair whorls and ear pattern. This paper also show an analysis of diverse biometric features study such as facial marks, facial features, iris, lip print, finger print, dental, ears and gait and also the different algorithms applied.