Improved AFIS for Color and Gray Image based on Biometric Triangulation

This research presents a fingerprint image processing algorithm for personal automatic identification, which has been in development since 1998. It is principally based on the comparison of the fingerprint’s biometric pattern between the fingerprint captured (original) in each session and the one stored in database. It is preferable to capture the image in color. The biometric pattern is formed by the Euclidean distances based on the triangulation of only three minutiae. This methodology locates the position and the type of each minutia to perform the triangulation. The applied metric is the statistic similarity obtained by the comparison of both biometric patterns. This technique enables one to solve translation and rotation problems. An original colored fingerprint is used in order to obtain more information about the fingerprint situation. The space color used is HCL, because it helps get a good skin color for an encrypt key, which is formed by each channel (HCL) in accordance with the skin color. This system has several applications due to its low cost and efficiency. Finally, the results obtained with this methodology were satisfactory since in all the experimental tests the system offered a rate of global success of 99 %.

[1]  Jie Tian,et al.  Ridge Distance Estimation in Fingerprint Images: Algorithm and Performance Evaluation , 2004, EURASIP J. Adv. Signal Process..

[2]  Darío Maravall Gómez-Allende,et al.  Reconocimiento de formas y visión artificial , 1993 .

[3]  Javier Ortega-Garcia,et al.  A review of schemes for fingerprint image quality computation , 2022, ArXiv.

[4]  Colegio De Ciencias Y Humanidades MAESTRIA EN CIENCIAS DE LA COMPUTACION , 1976 .

[5]  S. Cole Is Fingerprint Identification Valid? Rhetorics of Reliability in Fingerprint Proponents’ Discourse , 2006 .

[6]  A. Hanbury,et al.  A 3D-polar Coordinate Colour Representation Suitable for Image Analysis , 2003 .

[7]  Anil K. Jain,et al.  A Real-Time Matching System for Large Fingerprint Databases , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Anil K. Jain,et al.  Classification of Fingerprint Images , 1999 .

[9]  J. Angulo MORPHOLOGICAL COLOR PROCESSING BASED ON DISTANCES . APPLICATION TO COLOR DENOISING AND ENHANCEMENT BY CENTRE AND CONTRAST OPERATORS , 2005 .

[10]  M. C. Espino-Gudiño,et al.  Morphological multiscale contrast approach for gray and color images consistent with human visual perception , 2007 .

[11]  Adnan Amin,et al.  Coarse Fingerprint Registration Using Orientation Fields , 2005, EURASIP J. Adv. Signal Process..

[12]  David C. Hitchcock EVALUATION AND COMBINATION OF BIOMETRIC AUTHENTICATION SYSTEMS , 2003 .

[13]  Juan Manuel Miguel Jiménez,et al.  Sistema de reconocimiento de huellas dactilares , 2006 .

[14]  Antonio M. López,et al.  On Ridges and Valleys , 2000 .

[15]  Haim Levkowitz,et al.  GLHS: A Generalized Lightness, Hue, and Saturation Color Model , 1993, CVGIP Graph. Model. Image Process..

[16]  Charles E. H. Berger,et al.  Color Separation in Forensic Image Processing , 2006, Journal of forensic sciences.

[17]  Julian Ashbourn,et al.  Biometrics - advanced identity verification: the complete guide , 2000 .

[18]  James A. McHugh,et al.  Automated fingerprint recognition using structural matching , 1990, Pattern Recognit..

[19]  M. Sarifuddin A New Perceptually Uniform Color Space with Associated Color Similarity Measure for Content-Based Image and Video Retrieval , 2005 .

[20]  Michel Defrise,et al.  Symmetric Phase-Only Matched Filtering of Fourier-Mellin Transforms for Image Registration and Recognition , 1994, IEEE Trans. Pattern Anal. Mach. Intell..