Fingerprint Matching and Non–Matching Analysis for Different Tolerance Rotation Degrees in Commercial Matching Algorithms

A. J. Perez–Diaz*¹, I. C. Arronte–Lopez²

^1,2 Instituto Tecnológico y de Estudios Superiores de Monterrey, Campus Cuernavaca Autopista del Sol KM.104 C.P. 62790, Cuernavaca, Morelos, Mexico *E–mail: jesus.arturo.perez@itesm.mx

ABSTRACT

Fingerprint verification is the most important step in the fingerprint–based biometric systems. The matching score is linked to the chance of identifying a person. Nowadays, two fingerprint matching methods are the most popular: the correlation–based method and the minutiae–based method. In this work, three biometric systems were evaluated: Neurotechnology Verifinger 6.0 Extended, Innovatrics IDKit SDK and Griaule Fingerprint SDK 2007. The evaluation was performed according to the experiments of the Fingerprint Verification Competition (FVC). The influence of the fingerprint rotation degrees on false match rate (FMR) and false non–match rate (FNMR) was evaluated. The results showed that the FMR values increase as rotation degrees increase too, meanwhile, the FNMR values decrease. Experimental results demonstrate that Verifinger SDK shows good performance on false non–match testing, with an FNMR mean of 7%, followed by IDKit SDK (6.71% ~ 13.66%) and Fingerprint SDK (50%). However, Fingerprint SDK demonstrates a better performance on false match testing, with an FMR mean of ~0%, followed by Verifinger SDK (7.62% – 9%) and IDKit SDK (above 28%). As result of the experiments, Verifinger SDK had, in general, the best performance. Subsequently, we calculated the regression functions to predict the behavior of FNMR and FMR for different threshold values with different rotation degrees.

Keywords: biometry, fingerprints, matching, rotation, FMR, FNMR.

RESUMEN

La verificación de huellas dactilares es el proceso más importante en los sistemas de autenticación biométricos basados en huella dactilar. De acuerdo a la puntuación obtenida en la correspondencia de huellas se autentica o no a una persona. Actualmente existen dos métodos, muy populares, de correspondencia dactilar, correlación y minucias. En este artículo, se evaluaron tres sistemas biométricos basados en huella dactilar: Neurotechnology Verifinger 6.0 SDK Extended, Innovatrics IDKit SDK y Griaule Fingerprint SDK 2007. La evaluación se llevo a cabo de acuerdo a las pruebas efectuadas en la Fingerprint Verification Competition (FVC). Se evaluó la influencia de la tolerancia de los grados de rotación en las huellas dactilares en las tasas de falsa correspondencia (FMR) y falsa no correspondencia (FNMR). Los resultados muestran que los valores de FMR incrementan a medida que la tolerancia de los grados de rotación también lo hace, en contraparte los valores de FNMR disminuyen. Los resultados mostraron que Verifinger obtuvo un buen desempeño en las pruebas de falsa no correspondencia, con un promedio de 7%, seguido de IDKit (entre 6.71% y 13.66%) y Fingerprint SDK (50%). Fingerprint SDK obtuvo un desempeño superior en las pruebas de falsa correspondencia con un promedio cercano al 0%, seguido por Verifinger (entre 7.62% y 9%) e IDKit (28%). Como resultado Verifinger tuvo el mejor desempeño general. Posteriormente se calcularon las funciones de regresión para predecir el comportamiento de las tasas de falsa correspondencia y falsa no correspondencia con diferentes valores de tolerancia y grados de rotación.

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