Approaches to Fingerprint Matching

The approaches to fingerprint matching can be classified into three classes:
1. Correlation-based matching
- Superimpose images – compare pixels
2. Minutiae-based matching
- Classical Technique – Most popular
- Compare extracted minutiae


3. Ridge Feature-based matching
- Compare the structures of the ridges
- Everything else


Minutiae-based techniques locates minutiae points and then map their relative placement on the finger. However, this approach also has some drawbacks according to Salil Prabhakar et al. One being the fact that it is hard to extract the minutiae points accurately in case we have low quality fingerprint image. Also this approach does not take into account the global pattern of ridges and furrows (Salil Prabhakar et al). Although the correlation-based method is able to overcome some of the difficulties of the minutiae-based approach, it is still not an option since it requires an exact location of a registration point and are affected by image translation and rotation. Lastly, ridge-feature-based matching is superior to the other two and is based on such feature as orientation map, ridge lines and ridge geometry.

Drawbacks and Issues

So in the last post, we have tested the proposed algorithm on a small scale database and got some unexpected result. So in this post, we will justify our theories mentioned in the last post using tested results.

First, let's reiterate the problems. One of the issues is the match score for two different prints from identical fingers of the same individual was high while it should have been zero or very close to zero. This problem is plainly illustrated in the table below. Looking at row 1, column 2, we get a very high match score while we expect a really low value. Ironically, in column 3, and 4 we get really low values while we should have been getting a high value because now we are comparing the prints of two different individuals.

In the last post, we identified the problem to be the cropping and its size. Well, we were partially right! Let's look at the the cropped images for all four images:

Visually image 3 and 4 are very similar but their match score is still too high (5.0642). We still expect lower!

Note in cropped images above, the core is only visible in image 3 and 4; even there, we do not have the complete view of it.

So according to our experimental result and after careful examination of the source code, it seems that Dr. Yang is using correlation-based matching technique in which he superimposes the images and compare pixels. This technique cannot be used for our application because it is not invariant to translation and rotation. Thus, for the time being we drop this algorithm as an option and we pursue an alternative.