## 4 Experimental Evaluation--RIOTA's Implementation and Cork as a UNO
### Results
#### 4.4.1 Discussion  
This section details the discussion about the results reported in the previous one. The first test, referred to as the illumination setup--lighting test, tries to evaluate the influence of the light on capturing valuable information for the recognition task. The results are presented in the bar chart in Figure 3. From this bar chart, it is possible to observe that the number of matched descriptors is always higher with the light turned on. This is an expected behavior, since the illumination was designed to enhance the quality of the information present on the surface of cork stopper. The illumination ring arranged for illuminating tangentially with grazing light from the periphery towards the centre of the cork stopper suppresses inconvenient shadows and creates high-contrast images which leads to a \"suitable environment\" and potentiates the usage of local feature detectors and extractors like ORB for the recognition task. In this context, for the authors, a \"suitable environment\" is the circumstance in which the illumination creates areas where the valleys remain dark and the surface gets lighter which are promising areas for the Harris corner detector, often used in the local feature detectors. The second test seeks to evaluate how stable and repeatable the detected keypoints are in the images captured using the illumination device across seven different camera sensors. As before, the results are displayed as a bar chart shown in Figure 4. For this test, only 10% best detected keypoints were used to match the images, and RIOTA's implementation was tuned to ensure that the pairs of matched keypoints from one image to another were correctly matched. Let us assume a value of \(decisionThreshold=13\) (i.e., the threshold used to decide if two images represent the same object or not) which is the minimal threshold that ensures a perfect score (\(EER=0.0\%\), see Figure 5). In this scenario, all of the images taken by all the cameras used can still be correctly recognized. These results tend to indicate that the features are repeatable using different camera sensors, and this is achieved through the usage of the illumination device. The third test comprises the evaluation of the RIOTA's implementation over the cork database. The results are summarized in Figures 5 and 6, Table 1. The perfect score shown in Table 1 indicates that recognition Algorithm 1 is adequate for this application. Moreover, by analysing the evolution of both FAR and FRR along different values of the decision threshold, as seen in Figure 5, it can be verified that a decision threshold in the interval results in a \(EER=0.0\%\). This fact suggests that it is possible to choose a threshold up to a certain degree of confidence that does not compromises both FAR and FRR. These kind of measurements, including accuracy, EER, FAR and FRR, are often used to evaluate biometric recognition systems. As previously described, in the context of PUD systems, two measures are usually utilized: inter-distance and intra-distance. Commonly, these distances are represented using histograms and described by its statistical mean and standard deviation \((\mu_{intra},\sigma_{intra})\) and \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Acc (\%)** & **EER (\%)** & **FAR (\%)** & **FRR (\%)** \\ \hline Texture code matrix [50] & 100 & 0.00 & - & - \\ Triplet Half-Band Filter Bank (THFB) + k-out-of-n [51] & 98.06 & - & 1.99 & 1.88 \\ Proposed approach: RIOTA’s (Recognition of Individual Objects using Tagless Approaches) implementation & 97.59 & 2.45 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results from the MMU1 database [3].