Multidimensional nearest neighbors classification based system for incomplete lip print identification

Abstract

This paper presents a method of personal identification via the analysis of lip print images. This remains a little explored field even within the most serious and leading biometric research teams. Biometrics is the scientific study of the identification and verification of individuals based on their physiological and behavioral traits. Such traits are permanent, unique, and can be used to separately identify any one individual from any another. In the method we present here, we integrated complex image processing techniques, machine learning, and statistical methods. We then evaluated this new method on previously collected realistic (i.e. low quality) real-world lip print images. Multi-variant experimental protocols, specifically designed for this work, then confirmed the accuracy of our new technique. Our results have extended the knowledge of – and the collection of methods available for – the identification of biometric objects from incomplete data sets. Biometric analysis techniques are rapidly gaining in importance. The approach we propose here will be useful in many areas including biometrics, forensics, and forensic medicine. The novelty of this proposed method is its ability to work on lip print images that are partially corrupted or incomplete, as often occurs in practice. Low quality areas are recognized and effectively eliminated. These areas are neither taken into account during the classifier learning process nor later during the samples’ classification. This was confirmed in a series of experiments in which the best classification accuracy achieved was 94.40%.