## 4 Experimental Evaluation--RIOTA's Implementation and Cork as a UNO
### Results
#### 4.4.1 Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Databases** & **Detection Time (ms)** & **Extraction Time (ms)** & **Matching Time (ms)** & **Total Time (ms)** \\ \hline Cork database & 7.88 & 5.99 & 56.1 & 70.0 \\ CASIA-IrisV1 database & 2.60 & 3.64 & 32.3 & 35.3 \\ IITD database & 4.17 & 4.38 & 37.5 & 46.5 \\ MMU1 database & 2.34 & 3.40 & 12.5 & 18.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Mean time results of RIOTA’s implementation on the private and public databases—1 vs. 1 comparison. (CPU: Intel(R) Core(TM) i7-4790K CPU @ 4.00 GHz; OS: Xubuntu 16.04 LTS)\((\mu_{inter},\sigma_{inter})\). Figure 6 shows the normalized histograms for both distances in the cork database. As it can be seen, there is no overlap in the histograms, which is coherent with the error rate plot, and the low value of inter-distance achieved reinforces the idea of randomness and uniqueness of the cork. Moreover, since intra-distance provides the notion of evaluation noise, and there are no false negatives, this reinforces the idea of the success of capturing repeatable and stable features using the illumination device. The noise present in the acquired images from different camera sensors is not enough to compromise the recognition task. This also suggests some camera invariance which is a good indicator of this system. To evaluate the generalization capability of RIOTA's implementation, the fourth test was carried out using different iris databases found to be state of the art. Regardless of the fact that the recognition Algorithm 1 was not designed for the iris recognition problem, the results shown in Tables 2-4 are capable of competing with the techniques found in the literature, which were specially designed for iris recognition. An important note to be highlighted is: the input image for the RIOTA's implementation is a full size image and not the segmented iris as it should be. This decision was made to boost the development and testing process, considering that iris segmentation is out of the scope of this work. However, the results would most likely improve if the segmented image was used instead of the full size image. By visually inspecting all databases, it was observed that the photos with higher quality (i.e., higher definition, focused photos, etc.) belong to the IITD iris database, and there are some low quality photos (i.e., blurred/unfocused images) in the CASIA-IrisV1 database and MMU1 iris database. That might be the reason why the best recognition results are reported for the IITD database (Table 3). This fact is coherent to the method used (ORB) taking into account the specificities of the recognition algorithm. The keypoint detection is based on the Harris corner detection, and for blurred images, this detector is not very capable of detecting the stable keypoints necessary for the extraction and matching task. Therefore, the recognition results drops on both the CASIA-IrisV1 and MMU1 iris databases. From these results, it is possible to infer that RIOTA's implementation possesses a certain degree of generalization, with an identified limitation (the blurrier the image is, the more likely it is that the recognition system fails). For any type of recognition systems (e.g., biometrics, anti-counterfeiting systems) the time spent on the searching/matching process is crucial. The time needed to validate the identity of a person or perform a product authenticity check can compromise the applicability in a real-world scenario. For that reason, the time needed to match two images was measured and is shown in Table 5. This time includes the detection, extraction in two images, matching descriptors, and outlier removal with RANSAC. Let us assume a scenario where the database is populated by 1,000,000 images and the total time of 70 ms is a representative time for this recognition system. In this scenario, the total time needed to perform 1,000,000 comparisons is \(\sim\)19.4 h. Naturally, this result raises real-world applicability issues for this system.