Biometric Identification Based on Hand-Shape Features Using a HMM Kernel

Abstract

The present work presents a biometric identification system for hand shape identification. The different contours have been coded based on angular descriptions forming a Markov chain descriptor. Hidden Markov Models (HMM), each representing a target identification class, have been trained with such chains. Features have been calculated from a kernel based on the HMM parameters descriptors. Finally, supervised Support Vector Machines were used to classify parameters from the HMM kernel. Firstly, the system was modelled using 60 users to tune up the HMM and HMM+SVM configuration parameters and finally, the system was checked with all database, 144 users with 10 samples per class. Our experiments have obtained similar results per both cases, showing a scalable, stable and robust system. Our experiments have achieved an upper success rate of 99.92%, using four hand samples per class for training mode, and six hand samples for test mode. This success was found using as features the transformation of 100 points hand shape with our HMM kernel, and as classifier Support Vector Machines with lineal separating functions.