G. Aguilar-Torres*, G. Sánchez-Pérez, K. Toscano-Medina, H. Pérez-Meana

Sección de Estudios de Posgrado e Investigación, ESIME Culhuacan, Instituto Politécnico Nacional, México, D. F., México *gaguilar@ipn.mx.

Abstract

Person identification systems based on fingerprint patterns called Automatic Fingerprint Identification Systems, AFIS, are some of the most widely used biometric methods since they provide a high degree of success. The accuracy of AFIS is mainly due to some unique characteristics called minutiae, which are points where a curve track finishes, intersects with another curve track, or branches off. During past decades several efficient minutia-based fingerprint recognition algorithms have been proposed which achieve false recognition rates close to 1%, however, their recognition rate may be still improved. To this end, this paper presents a fingerprint recognition method using a combination of the Fast Fourier Transform (FFT) with Gabor filters for image enhancement. Next, fingerprint recognition is carried out using a novel recognition stage based on Local Features and Hu invariant moments for verification.

Keywords: AFIS, FFT, Gabor filters, Hu invariant moments, minutiae, recognition.

Resumen

Los sistemas de identificación de personas basados en huellas dactilares AFIS (Automatic Fingerprint Identification System), son uno de los sistemas más utilizados ya que proporcionan un alto grado de exactitud. La exactitud de un AFIS es debido principalmente a sus características únicas, llamadas minucias, las cuales son puntos donde los bordes terminan o se dividen. En los últimos años, varios algoritmos de reconocimiento de huella dactilar han sido propuestos los cuales alcanzan porcentajes de falso reconocimiento de alrededor del 1%. Sin embargo, este porcentaje puede ser mejorado. Para esto, este artículo presenta un método de reconocimiento de huella dactilar usando una combinación de Transformada Rápida de Fourier (FFT) y filtros de Gabor para un mejoramiento de la imagen. Después, el reconocimiento es realizado usando una novedosa etapa basada en características locales y momentos invariantes Hu para verificación.

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