| Recent years, Fingerprint recognition as an important part of Biometric recognition catch people's attention all along. From 1960s to now, many fingerprint recognition algorithms have been designed, and a number of biometric products have been developed, but fingerprint recognition is a complex pattern recognition problem, further more, some technology is kept secret for commercial interest, to implement a high-powered AFIS (Fingerprint Identification System) is still a very challenge task. In this paper, all the progress made in automatic techniques for fingerprint recognition are been analyzed, a suit of innovated approaches and an effective fingerprint recognition system have been designed.Fingerprint recognition mainly includes five parts: Fingerprint Representation, Feature Extraction, Fingerprint Classification, Fingerprint Matching, and Fingerprint Image Compress.Fingerprint Representation aims at getting rid of the noise in the fingerprint image, connecting the rupture of ridge and eliminating fingerprint image's distortion. First, we estimate the local ridge orientation and local ridge noise by the local gray grads at different orientation. We use a window to compute local gray grads, but the size of the window will affects the result. Using bigger window can put down the bad effect caused by noise for local ridge orientation, but the local ridge orientation we get is low precision. Otherwise, using smaller window can put down the bad effect caused by noise for local ridge orientation, but the local ridge orientation we get is high precision. We can give a good balance, according to the local ridge noise, if with the noise, the smaller window can be used, we can get a precise orientation. If not, the bigger window is adopted and we get the most precise orientation. We import Local Ridge Frequency describing how dense the ridge is. We use Gabor Filter to enhance fingerprint image. Gabor transform is a Fourier transform which window function is Gaussian function. Gabor transform has good character in time and frequency territory. We make Gabor Filter's frequency accords with the local ridge frequency and orientation accords with the local ridge orientation. With these setting, Gabor Filter can improve fingerprint image markedly. For turning improved fingerprint image to binary image, we use special filter window that holds more than 2 local ridges and takes the same orientation as the local ridge. We take the parallel thinning method presented by A Datta and S.K Parui to get thin image based on binary image.The characters we detection include Singularity, Minutiae, Ridge Number and Shadow points. The algorithms based on Poincare Index can detection Singularity most decently and effectively, but it is affected by the noise. We use "centrality intensity coefficient" to correct the Singularity. The true Singularity's centrality intensity coefficient is especially larger than the false one. For Minutiae detecting, first we shall create line between the two characters by the algorithms presented by Bresenham, In practice, the cross number of two curve can be 0 or more than 1. In this paper, the algorithms of COLAL (cross of line and line) are presented. It used can patch the skipped point and eliminate the continuous points. For detecting Minutiae, the cross number Cn(p) and ridge number Sn(p) are imposed, Cn(p) is the change number of ridges and valleys around the point p. Sn(p) is the ridge number around the point p. When Cn(p)=1,Sn(p)=1,p is ridge endings; When Cn(p)=2, Sn(p)=2或3或4,p is a continuous point；When Cn(p)=3,Sn(p),p is ridge bifurcations. Because the existence of false characters such as burrs, eyes, bridges, we should utilize the techno-ridge-tracing to eliminate false minutiae, according to techno-ridge-tracing do the follow: 1. when tracing the ridge endings and the tracing distance being smaller than a threshold, there would be a burr, wiping off the current point and burr. When the superposition ridge bifurcations occur, there would be an eye between the two the current s...
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