# Invariant Feature Encoding for Contact Handprints Using Delaunay Triangulated Graph
## 4 Experiments
### Feature Extraction
#### 4.2.1 Matching  
It is noted that contact fingerprint state-of-the-art methods primarily focus on minutiae-based triplets, quadruplets, and minutiae-pair matching. However, there is a possibility for matching two triangles/quadruplets in different locations of the images with the same features. The work based on the Delaunay triangulation matching in the past work performed triangle-triangle matching, and the scores were computed using only initial matched triangles. There is no mechanism for checking reliability of the initial matched triangles. Further, the existing matching strategies cannot directly support both partial prints and latent prints.  
The work in this paper has performed a few further steps to (i) check the reliability of such initially matched triangles; (ii) extend the triangles form several sub-graphs; (iii) eliminate unreliable sub-graphs; and (iv) fuse the disjoint sub-graphs based on a validation threshold. The threshold value is generated based on the number of minutiae each graph holds.  
Figure 7: Minutia localization of a palm print image: Subfigures (**a,b**) illustrate enhanced image and localized minutiae set, respectively. Note that the minutiae set in subfigure (**b**) consists of genuine and spurious minutiae. The yellow spots highlight endpoints, while red spots indicate bifurcation of ridges. To evaluate the algorithm’s applicability with the palm, a hypothenar segment of a palm print was used to minimize the overloaded minutiae.  
The assumption made here is that if there is a match between two triangles in two images captured from the same individual, then the related information around that particular triangle with all the other neighboring triangles will also be matched. Then, the initial matched triangles can grow further, and there is a possibility for the formation of several sub-graphs based on the initial triangles, and these sub-graphs can be identified in different hand locations.  
In contrast, if there is a triangle match between two images captured from two different individuals, there would be a possibility of having less extension of the initial triangle, and it would form fewer sub-graphs. It is obvious that the volume of sub-graph growth would be small and it would stop early in the process. In this point of view, the minutia-based matching triplets obtained from the initial phase were checked.  
In the first phase of the algorithm, base triangles (initially matched triangles) were formed by matching Delaunay triangles between the template and probe pairs. An illustration of the phase 1 of the algorithm with a set of matched triangles is shown in Figure 3. It can be observed that some of these triangles do not actually match the other triangles in the counter part, and the matched triangles may or may not be reliable, as spurious minutiae can have significant effects in the process of initial triangle formation. Therefore, there is a need for another step to check the reliability of the initially matched triangles in Figure 3. They were validated in the second phase of the method. At the end, a collection of sub-graphs is generated, as illustrated in Figure 5. False sub-graphs were eliminated using the threshold number of minutiae each graph holds. The extracted sub-graphs were then merged, and DTG was formed.