Open AccessANALYSIS OF FACIAL MARKS TO DISTINGUISH BETWEEN IDENTICAL TWINS USING NOVEL METHOD
Author(s) -
G. Hari Krishna,
Rishi Kumar
Publication year - 2013
Publication title -
international journal of communication networks and security
Language(s) - English
Resource type - Journals
ISSN - 2231-1882
DOI - 10.47893/ijcns.2013.1075
Subject(s) - biometrics , computer science , artificial intelligence , face (sociological concept) , pattern recognition (psychology) , similarity (geometry) , facial recognition system , usability , identical twins , identity (music) , computer vision , image (mathematics) , human–computer interaction , biology , physics , social science , sociology , acoustics , genetics
Reliable and accurate verification of people is extremely important in a number of business transactions as well as access to privileged information. The biometrics-based methods assume that the physical characteristics of an individual (as captured by a sensor) used for verification are sufficiently unique to distinguish one person from another. But the increase in twin births has created a requirement for biometric systems to accurately determine the identity of a person who has an identical twin. Identical twins have the closest genetics-based relationship and, therefore, the maximum similarity between fingerprints is expected to be found among identical twins. They can’t be discriminated based on DNA. As one of the most successful applications of image analysis and understanding, face recognition has recently received significant attention, especially during the past several years. Identical twin face recognition is a difficult task due to the existence of a high degree of correlation in overall facial appearance. In this paper, we study the usability of facial marks as biometric signatures to distinguish between identical twins. We propose a multi scale automatic facial mark detector based on a gradient-based operator known as the fast radial symmetry transform. The transform detects bright or dark regions with high radial symmetry at different scales. Next, the detections are tracked across scales to determine the prominence of facial marks. Extensive experiments are performed both on manually annotated and on automatically detected facial marks to evaluate the usefulness of facial marks as biometric signatures. The results of our analysis signify the usefulness of the distribution of facial marks as a biometric signature.