# Invariant Feature Encoding for Contact Handprints Using Delaunay Triangulated Graph
## 5 Results
### Handprint Segments from Palm Region
#### 5.2.2 Experiment Using Extremely Degraded Images with Geometrical Variations  
In practice, the image degradation in contact-based impressions is large in scale. Particularly, in forensic scenarios, the image quality will be worse than the other contact prints. Therefore, we have conducted a separate experiment using low-quality images that are similar to forensic impressions, as illustrated in Figure 2. Extremely-low-quality and degraded palm print segments have been used in this experiment. For this experiment, a set of images from THUPALMLAB database with 320 templates and probes from 40 users has been used. This generated the scores of 2233 genuine and 99,525 impostor matches. A performance metric of 80% GAR at a point of 5% FAR is observed, and an EER of 6.94% is reported with this data set. Figure 9 illustrates the comparison of the above performance. Table 2 summarizes the results acquired from palmprint recognition for all three different conditions. It is observed that when there is an increased number of partial-quality images, the GAR is slightly increased. Even though the impressions from the extremely-low-quality and degraded data set shows comparatively lower performance in term of EER, there is potential to use the algorithm with degraded prints and extend it to the forensic domain in future. Since our focus is to investigate how well the proposed approach can work with extreme cases in contact prints that resemble latent prints, the method still gives a positive motive to propose it towards general and forensic applications. It is obvious that the extreme cases are not always the majority in these applications. Therefore, the results indicate that the algorithm can cope with that scenario as well. Further, the comparison result of the algorithm outperformed the state-of-the-art SIFT features as outlined in Table 3. In that perspective, the proposed method can be acceptable to a certain level. It is noted that the state-of-the-art matching algorithms for the contact prints (fingerprint or palm print) were specifically implemented for full or partial prints in a single platform, which focuses on achieving best performance using full data set at any cost. On the other hand, the contactless domain explores Convolutional Neural Network (CNN)-based methods in addition to multi-view, multi-modal and multi-resolution strategies to achieve the best performance at any cost. The CNN-based methods need a larger size of data to overcome the over-fitting issue during training the model. Since our focus is not \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Palmprint Quality** & **No. of Genuine Tests** & **No. of Impostor Tests** & **GAR** \\ \hline Partially degraded impressions with & & & \\ geometrical variations & 1400 & 38,400 & 88.5\% \\ (25 users) & & & \\ \hline Partially degraded impressions with & & & \\ geometrical variations & 1960 & 76,160 & 90\% \\ (35 users) & & & \\ \hline Extremely degraded impressions & & & \\ with geometrical variations & 2233 & 99,525 & 80.33\% \\ (40 users) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Matching performance on different levels in palm print degradation with geometrical variations at 5% of FAR. proposing an algorithm for fingerprints or palm prints only but investigating a common approach for contact handprints, the existing work in contact-based approaches is only considered for the comparison investigation. Minutia-based and texture-based techniques are the majority of the existing work. The Gabor, SIFT, CompCode and its variants (RLOC, binary orientation co-occurrence vector (BOCV)) algorithms used the orientation of palm lines as the palm print features by using the Gabor filter. To extract the Gabor features, the ROI should have been already defined, and the images should be aligned based on any key points. This is the main issue in the majority of the filter-based methods, along with the computational cost [39; 40; 41].