Tommy Truong

Objective : To enhance noisy fingerprint images in order to be processed by an automatic fingerprint recognition system, which extracts the minutiae of the fingerprint. Motivation: Biometric security, law enforcements, and personal identification.

The uniqueness of a fingerprint can be determined by the pattern of ridges and furrows as well as the minutiae points. Minutiae points are local ridge characteristics that occur at either a ridge bifurcation or a ridge ending. Ridge ending is point where a ridge ends abruptly. Ridge bifurcation is e point where a ridge forks or diverges into branch ridges

L. Hong, Y. Wan, and A. Jain, “Fingerprint Image Enhancement: Algorithm and Performance Evaluation”

Normalization reduces the variations in gray-level values along ridges and valleys. Segmentation makes the distinction between ridges and nonridges regions.

Intrinsic property of fingerprint images. Defines invariant coordinates for ridges and valleys in a local neighborhood. Helps fill in gaps that in the ridges.

Another intrinsic property of fingerprint images. In a local neighborhood where no minutiae and singular points appear, the gray levels along ridges and valleys can be modeled as a sinusoidal-shaped wave along a direction normal to the local ridge orientation. Used to identify recoverable and unrecoverable regions.

Gabor filtering is used, which is a bandpass directional filter. Uses the ridge frequency image and the orientation image. Smoothes out the ridges and makes the fingerprint more clear and reduces noise.

Allows the minutiae to be extracted much more efficiently in an automatic fingerprint recognition system.

Using Verifinger, the Fingerprint Acceptance Rate with database of 7 different fingerprints (unenhanced) and test sample of 49 fingerprints (unenhanced, 7 from each person) is 96% (47/49). With the database unenhanced, and test samples enhanced, FAR = 94% (46/49) With the database enhanced and test samples unenhanced, FAR = 92% (45/49). With both test samples enhanced and database enhanced, FAR = 90%. (44/49).

Enhancement code might have created additional minutiae or removed minutiae from the original image. Verifinger algorithm could be reason for the inconsistencies. Fingerprints used could also be the reason for the inconsistencies.

-Fingerprints are more complicated than I thought. -Enhancement can literally affect people’s lives. -Fingerprint enhancement on extremely noisy fingerprints would have no result. Pros: - Output looks much clearer. Cons: - Too slow, ~4 seconds/image - Created gaps that weren’t suppose to be there or filled gaps that weren’t suppose to be filled

Refine the local orientation estimation code Try different filters. Use this to implement a fingerprint recognition system. Evaluate using goodness index or other methods for a more accurate evaluation.