# Invariant Feature Encoding for Contact Handprints Using Delaunay Triangulated Graph
## 6 Conclusions  
The investigation on the DTG algorithm gives a rationale for the methodology and validates the claims using impressions from handprints with a range of image quality and geometrical variations. It works well for fingerprint segments with geometrical variations. For the palm print segments, it demonstrates how far it works well with partial-quality and extremely-low-quality handprints while using only simple minutiae features. The handprint segments completely show geometrical variations, and the DTG is not very sensitive to similarity transformations present in the database. They resulted in comparatively better performance than the state-of-the-art method as well. Even though the performance with extremely degraded prints is comparatively less than the partial-quality prints, there is potential to use the DTG in extreme cases of the contact prints because extreme degradation is not always the majority case for contact prints. Further, it has an advantage to leverage the method in multiple regions of the handprints where poor quality regions would be merged with good quality regions of the hand. Therefore, the algorithm can be acceptable in algorithmic and application contexts.  
In the algorithmic context, the proposed method uses only minutiae. However, there is a possibility for fusing other features if the quality of the images is good enough to show ridge information clearly. The processes of one-to-one minutiae match and false minutiae removal are replaced by graph match and graph validation that eliminate unnecessary computations for false minutia removal. In the application context, if any prints that show not only minutiae but any minutiae-like point-related information, that can benefit from the algorithm. As the method is invariant to geometrical transformations, any segment of the hand can be treated without manual alignment of the compared images.  
The significance of the DTG is that it would be advantageous to adopt contact-based handprint system in access control and other civilian applications. On the other hand, the similar procedure can be adopted in forensic environment as it generally shows extremely-low-quality prints and forensic experts do not know which part of the hand the prints are obtained from. In that scene, the algorithm can produce some likelihood similarity for the matching images if they are too far degraded. In an overall context, the DTG is the output for the concept of one single algorithm for one hand.  
Conceptualization, A.J.M.A.C. and V.C.; Methodology, A.J.M.A.C. and V.C.; Software, A.J.M.A.C.; Validation, A.J.M.A.C.; Resources, J.B.; Writing--original draft, A.J.M.A.C.; Writing--review & editing, J.B. and V.C.; Supervision, J.B. and V.C. All authors have read and agreed to the published version of the manuscript.  
The research received no external finding.  
Not applicable.  
The authors declare no conflict of interest  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Palm Print Quality Type** & **EER Using DTG** & **EER Using SIFT [16]** \\ \hline Partially degraded impressions with geometrical variations (25 users) & 5.76\% & 7.60\% \\ \hline Partially degraded impressions with geometrical variations (35 users) & 5.69\% & 6.44\% \\ \hline Extremely degraded (low-quality) impressions with geometrical variations (40 users) & 6.94\% & 10.85\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of performance between the DTG algorithm and the state-of-the-art SIFT features [16].