Pupil size as a biometric trait

Abstract

We investigate the possibility of using pupil size as a discriminating feature for eye-based soft biometrics. In experiments carried out in different sessions in two consecutive years, 25 subjects were asked to simply watch the center of a plus sign displayed in the middle of a blank screen. Four primary attributes were exploited, namely left and right pupil sizes and ratio and difference of left and right pupil sizes. Fifteen descriptive statistics were used for each primary attribute, plus two further measures, which produced a total of 62 features. Bayes, Neural Network, Support Vector Machine and Random Forest classifiers were employed to analyze both all the features and selected subsets. The Identification task showed higher classification accuracies (0.6194÷70.7187) with the selected features, while the Verification task exhibited almost comparable performances (~ 0.97) in the two cases for accuracy, and an increase in sensitivity and a decrease in specificity with the selected features.