Implementation of fingerprint image enhancement filters. This repo contains two different filtering tools (gabor and wahab) and a library file for common utility functions (utils.py). Below is a general description of all three files. For more information, please read the source code.
An executable script file that contains the code for applying the Wahab filter. Based on (and named after) WCT98. It consists mainly of a function called wahabKernel() that creates a directional kernel for a given orientation, and a function called wahabFilter() that divides the image into cells, and convolves each cell with a directional kernel corresponding to the average orientation of the cell.
An executable script file that contains the code for applying the Gabor filter. Based on HWJ98. It contains the gaborKernel() function that creates a Gabor kernel for a given orientation and frequency. It contains two functions, gaborFilter() and gaborFilterSubdivide() processes the image by cell iteration or by area subdivision, respectively. They both
divide the image into smaller chunks, and convolve each chunk with a Gabor kernel corresponding to the average orientation in the chunk.
A Python file that is not meant to be invoked directly, but imported into other scripts. It contains a number of commonly useful functions for fingerprint image enhancement. The most important functions are:
A custom convolution function that allows us to convolve a whole image, or just a sub-area of an image.
Marks areas as good or bad, depending on the standard deviation of values within the area.
Creates an orientation field for an image, using a combination of the methods from HWJ98 and SMM94.
Createsafrequencyfieldforanimage,usingthemethod from [HWJ98].
Sherlock, BG; Monro, DM; Millard, K: Fingerprint enhancement by directional Fourier filtering. IEE Proceedings-Vision, Image and Signal Processing, 141(2):87–94, 1994.
Hong, Lin; Wan, Yifei; Jain, Anil: Fingerprint image enhancement: Algorithm and performance evaluation. IEEE transactions on pattern analysis and machine intelligence, 20(8):777–789, 1998.
Wahab, A; Chin, SH; Tan, EC: Novel approach to automated fingerprint recognition. IEE Proceedings-Vision, Image and Signal Processing, 145(3):160–166, 1998.