Transformation of Hand-Shape Features for a Biometric Identification Approach

Sensors

Carlos M. Travieso¹  Juan Carlos Briceño² 

[1] Signals and Communication Department, Institute for Technological Development and Innovation in Communications, University of Las Palmas de Gran Canaria, Campus Universitario de Tafira, Ed. de Telecomunicación, Pabellón B, Despacho 111., Las Palmas de Gran Canaria 35017, Spain; E-Mail:;Computer Science Department, University of Costa Rica, San Jose, Costa Rica; E-Mail:

关键词: hand-based biometrics;    hand identification;    DHMM kernel;    supervised classification;    image sensor;    edge detection;    biometrics;   

DOI  :  10.3390/s120100987

来源: mdpi

【 摘 要 】

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. Discrete Hidden Markov Models (DHMM), each representing a target identification class, have been trained with such chains. Features have been calculated from a kernel based on the HMM parameter descriptors. Finally, supervised Support Vector Machines were used to classify parameters from the DHMM kernel. First, the system was modelled using 60 users to tune the DHMM and DHMM_kernel+SVM configuration parameters and finally, the system was checked with the whole database (GPDS database, 144 users with 10 samples per class). Our experiments have obtained similar results in both cases, demonstrating a scalable, stable and robust system. Our experiments have achieved an upper success rate of 99.87% for the GPDS database using three hand samples per class in training mode, and seven hand samples in test mode. Secondly, the authors have verified their algorithms using another independent and public database (the UST database). Our approach has reached 100% and 99.92% success for right and left hand, respectively; showing the robustness and independence of our algorithms. This success was found using as features the transformation of 100 points hand shape with our DHMM kernel, and as classifier Support Vector Machines with linear separating functions, with similar success.

【 授权许可】

CC BY   
© 2012 by the authors; licensee MDPI, Basel, Switzerland