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.