Hand shape identification on multirange images

Abstract

A hand-shape based biometric identification system which is independent of the image spectrum range is proposed here. Two different spectrum ranges; visible and mid-range infrared, were used to validated the architecture, which maintained the accuracy and stability levels between ranges. In particular, three public databases were tested, obtaining accuracies over 99.9% using a 40% hold-out cross-validation approach. Discrete Hidden Markov Models (DHMM) representing each target identification class was trained with angular chain descriptors. A kernel was then extracted from the trained DHMM and applied as a feature extraction method. Finally, supervised Support Vector Machines were used to classify the extracted features.