Fingerprint Image Segmentation Method Based on MCMC&GA

Fingerprint image segmentation is one key step in Automatic Fingerprint Identification System (AFIS), and how to do it faster, more accurately and more effectively is important for AFIS. This paper introduces the Markov Chain Monte Carlo (MCMC) method and the Genetic Algorithm (GA) into fingerprint image segmentation and brings forward a fingerprint image segmentation method based on Markov Chain Monte Carlo and Genetic Algorithm (MCMC&GA). Firstly, it generates a random sequence of closed curves, which is regarded as the boundary between the fingerprint image region and the background image region, as Markov Chain, which uses boundary curve probability density function (BCPDF) as the index of convergence. Then, it is simulated by Monte Carlo method with BCPDF as a parameter, which is converged at the maximum. Lastly, Genetic Algorithm is introduced to accelerate the convergent speed. In conclusion, the closed curve with the maximum value of the BCPDF is the ideal boundary curve. The experimental results indicate that the method is robust to the low-quality finger images.