title: On the discriminability of the handwriting of twins.
abstract: As handwriting is influenced by physiology, training, and other behavioral factors, a study of the handwriting of twins can shed light on the individuality of handwriting. This paper describes the methodology and results of such a study where handwriting samples of twins were compared by an automatic handwriting verification system. The results complement that of a previous study where a diverse population was used. The present study involves samples of 206 pairs of twins, where each sample consisted of a page of handwriting. The verification task was to determine whether two half-page documents (where the original samples were divided into upper and lower halves) were written by the same individual. For twins there were 1236 verification cases -- including 824 tests where the textual content of writing was different, and 412 tests where it was the same. An additional set of 1648 test cases were obtained from handwriting samples of nontwins (general population). To make the handwriting comparison, the system computed macro features (overall pictorial attributes), micro features (characteristics of individual letters), and style features (characteristics of whole-word shapes and letter pairs). Four testing scenarios were evaluated: twins and nontwins writing the same text and writing different texts. Results of the verification tests show that the handwriting of twins is less discriminable than that of nontwins: an overall error rate of 12.91% for twins and 3.7% for nontwins. Error rates with identical twins were higher than with fraternal twins. Error rates in all cases can be arbitrarily reduced by rejecting (not making a decision on) borderline cases. A level of confidence in the results obtained is given by the fact that system error rates are comparable to that of humans (lower than that of lay persons and higher than that of questioned document examiners [QDEs]).
authors: Department of Computer Science and Engineering and Director, Center of Excellence for Document Analysis and Recognition, University at Buffalo, State University of New York, Buffalo, NY 14228, USA. Srihari@cedar.buffalo.edu
authors: Sargur
authors: S
authors: Srihari
authors: Chen
authors: C
authors: Huang
authors: Harish
authors: H
authors: Srinivasan
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D002648:Child; D002675:Child, Preschool; D005260:Female; D005554:Forensic Medicine; D006236:Handwriting; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D016013:Likelihood Functions; D008297:Male; D012984:Software; D014427:Twins