Complex spectral minutiae representation for fingerprint recognition

Abstract

Abstract

Minutiae-based Fingerprint Extraction and Recognition

“…The orientation θ of a minutia can be incorporated by using the spatial derivative of m(x, y) in the direction of the minutia orientation. Thus, to every minutia in a fingerprint, a function m i (x, y, θ) is assigned being the derivative of m i (x, y) in the direction θ i , such that (4) As with the SML algorithm, using a Gaussian filter and taking the magnitude of the spectrum yields (5) Recently, (Xu & Veldhuis, 2010) have further discussed the objective of the spectral minutiae representation in representing a minutiae set as a fixed-length feature vector that is invariant to translation, rotation and scaling. Fig.…”

“…14 illustrates a general procedure of the spectral minutiae representation discussed by (Xu & Veldhuis, 2010). (Xu & Veldhuis, 2010) Moreover, based on the spectral minutiae feature, (Xu et al, 2009a(Xu et al, , 2009b introduced two feature reduction methods: the Column-Principal Component Analysis (PCA) and the LineDiscrete Fourier Transform feature reduction algorithms. The experiments demonstrated that these methods decrease the minutiae feature dimensionality with a reduction rate of 94%, while at the same time, the recognition performance of the fingerprint system is not degraded.…”

On the use of spectral minutiae in high-resolution palmprint recognition

“…Recently, the spectral minutiae representation [1][2] has shown its power in minutiae-based fingerprint recognition, which can handle minutiae translation and rotation and improve matching speed, satisfying properties required by high-resolution palmprint recognition as well. As defined in [1], the method uses the minutiae locations in spatial domain and takes Fourier transform of the coded locations and obtains the magnitude of its Fourier spectrum in frequency domain.…”

“…As defined in [1], the method uses the minutiae locations in spatial domain and takes Fourier transform of the coded locations and obtains the magnitude of its Fourier spectrum in frequency domain. The three types of spectral minutiae representations are the location-based spectral minutiae representation (SML), the orientation-based spectral minutiae representation (SMO) and the complex spectral minutiae representation (SMC), among which the enhanced SMC method [2] performs best for fingerprints with the EER of 3.05% on FVC2002 DB2A 1 database and a matching speed of 8000 comparisons per second.…”

“…In this work, we first optimize key parameters for the SML and SMC method by experimental study on the characteristics of spectral minutiae representation using both fingerprints and palmprints. Then we implement the direct matching as stated in [1][2] for palmprints and evaluate the performance of SML and SMC respectively on the public high-resolution palmprint database THUPALMLAB [3].…”