## 3 Evaluation
### Results  
An evaluation of the complete system in a SSPP scenario with identity verification purposes was carried out for each database separately, allowing for the comparability of the results. In addition, a test where an image coming from the gb2s_Selfies dataset was compared against an image of the same user coming from the gb2s_IDCards dataset was included to evaluate this relevant and complicated scenario. Tables 5 and 6 gather the results obtained during de validation and test stages respectively. Results are also illustrated in Figures 1 and 2. It can be seen that capturing conditions have a great influence on the results. In fact, the results achieved on databases recorded under quite controlled capturing conditions, which usually present lower variability between images, are quite good for every model. However, as soon as the complexity of the datasets increases, the influence of the model become more evident. It can be observed that the most influencing conditions are lighting, in particular low light levels such as those present on Extended Yale B database, expressions and appearance of the users. The image size and the number of images per person does not seem to be very relevant. As can be expected, stronger differences between enrolment and access samples lead to worse results. It is clearly shown in the Selfies-IDCards scenario. Focusing on the feature extraction models, it can be seen that the progressive model reduction from 9 MB to 30 MB, 22 MB and 12 MB increases the averaged validation EER in 8.37%, 10.31% and 9.79%, respectively, while the averaged test FMR raises are 8.27%, 10.11% and 9.80%, respectively, and the averaged test FNMR increments are 8.81%, 10.74% and 10.94%, respectively. It is evident that a big and well trained model is required to deal with the most complicated scenarios. Accordingly, FaceNet is able to deal even with the Selfies-IDCards, providing quite acceptable results. Applications associated to this scenario typically have strong security requirements, and the use of a bigger model could be justified. However, more research efforts are required to determine a competitive solution in terms of model size and accuracy for this particular scenario. On the other hand, applications where resources saving is a priority need smaller models such as OpenFace and gb2s_model(s). The results achieved by these models do not present a clear pattern and they strongly depend on the dataset used during the evaluation. In one half of the cases, OpenFace surpasses gb2s_models, but, on the other half, the results are similar or even worse. Differences between gb2s Model1 and gb2s Model2 are not relevant, being a little bit better the gb2s Model2 in many cases, which is the smallest. Obtained results highlight that the use of a reduced model for face recognition is viable when they are trained with a sufficiently big dataset containing enough representativeness. Comparing validation and test results, a great consistency can be observed. The biometric template of each user is composed just by one single sample and the feature extraction models were trained with separated datasets, thus the learnt features were not directly related with the images used in the experiments and their capturing conditions. Therefore, validation and test results obtained for each dataset are pretty similar. Finally, even when comparison against the state of the art is difficult because different authors follow different evaluation methodologies, it can be seen that the presented results generally match or exceed the state-of-the-art solutions. Focusing on those works which use the same databases for evaluation purposes, it can be observed that FaceNet and OpenFace clearly surpasses the actual state of the art. This is not the case of gb2s_Model(s), which in some cases surpasses the state of the art but in many cases does not. However, it can be observed that most state-of-the-art solutions use the same databases for training and testing purposes, which could influence the results.