Combining Biometric Fractal Pattern and Particle Swarm Optimization-Based Classifier for Fingerprint Recognition

• Published in: Mathematical Problems in Engineering Vol. 2010; no. 1; pp. 1096 - 1109-063
• Main Authors: Lin, Chia-Hung, Chen, Jian-Liung, Gaing, Zwe-Lee
• Format: Journal Article
• Language: English
• Published: New York Hindawi Limiteds 01.01.2010; Hindawi Publishing Corporation; John Wiley & Sons, Inc
• Subjects: Algorithms; Biometric recognition systems; Biometrics; Classifiers; Deoxyribonucleic acid; Digital imaging; Digital signal processors; DNA; Fingerprint verification; Fingerprinting; Fingerprints; Fractal analysis; Fractal geometry; Fractals; Image processing; Mathematical analysis; Neural networks; Noise; Particle swarm optimization; Pattern recognition; Principal components analysis; Recognition
• ISSN: 1024-123X; 1563-5147
• DOI: 10.1155/2010/328676

This paper proposes combining the biometric fractal pattern and particle swarm optimization (PSO)-based classifier for fingerprint recognition. Fingerprints have arch, loop, whorl, and accidental morphologies, and embed singular points, resulting in the establishment of fingerprint individuality. An automatic fingerprint identification system consists of two stages: digital image processing (DIP) and pattern recognition. DIP is used to convert to binary images, refine out noise, and locate the reference point. For binary images, Katz's algorithm is employed to estimate the fractal dimension (FD) from a two-dimensional (2D) image. Biometric features are extracted as fractal patterns using different FDs. Probabilistic neural network (PNN) as a classifier performs to compare the fractal patterns among the small-scale database. A PSO algorithm is used to tune the optimal parameters and heighten the accuracy. For 30 subjects in the laboratory, the proposed classifier demonstrates greater efficiency and higher accuracy in fingerprint recognition.