# Evaluating Secure Methodology for Photo Sharing in Online Social Networks
## 9 Results and Discussion
### Discussion  
According to our experiment, we have 42 participants as users of apps and each participant has an account. As shown in Figure 10, the number of images posted by every user. The photos posted by users varied, with some relating to the participant himself and others relating to the participants in the experiment and others not participated. Images of  
Figure 14: TP, FN and FP of face recognition in app. Figure 13: Image Type. Figure 15: Reason behind not recognize the faces. some of the participants were from different ages and scenarios, as mentioned in Section 7. Figure 16 show's some participants posted photos of people, the application was able to identify people and send them notifications. It is clear from the results that our proposed system has been able to recognize people as shown in Figure 12 that represents the percent of recognition and non-recognition cases. 74.84% of photos were identified successfully, while 25.16% could not be recognized. We asked users to enter pictures of different age stages, some pictures of them in the case of tilting the face, pictures from far and near, and pictures with different resolutions. Figure 17 shows samples of posted photos, some of them recognize their owners and some of which are not. We have looked at the results of Figure 16 that indicates the number of recent and old photos released per user. CNN model had errors in the prediction process, since it classifies people to people other than what they belong to. This is illustrated in Figure 14.  
It is observed that possibility of identifying persons in new photos are more likely than in old photos. The reason behind this might be that the new images have gained a high recognition rate due to the narrow period between our stored image and the released image, and also developing imaging precision in modern devices facilitate reading the face features, for example Figure 17a. On the other hand, some of the photos were difficult to be identified using old photos if the pictures are of an early age or the appears and not the  
Figure 16: Recognized multiple persons. Figure 17: Samples of recognized and not recognized faces: (**a**) represents recognised faces and (**b**) represents unrecognised faces. complete face for example Figure 17b. accuracy of the pictures is not good enough, or part of the face. It is observed also that photos were taken with the close different stages of the age can be recognized easily, while the pictures with far stages of the age cannot be recognized. An example of close different stages of the age is shown in Figure 18a. Moreover, an example of long stages of the age is shown in Figure 18b. Additionally, it is observed that when we obtained at the time of registration a clear picture of the person in good lighting that shows the features of the face, we can identify him in different stages, provided that the face is completed and the accuracy of images is good, for example Figure 19.  
It is observed there was a challenge in the faces that do not appear completely, it was showing half the face, the face far away, or people who wear accessories. The success of recognition is due to the quality of the image captured in the registration. For example, in Figure 20a, a person was wearing sunglasses; in Figure 20b, a person was far; in Figure 20c,d, a person was looking left or right which means just have half of the face is appeared; therefore, the app was able to identify all of them. Figure 19: Samples of recognized different age stages of one person. Figure 20: Samples of success recognized in different shape: (**a**) represents wearing sunglasses, while (**b**) represents so far shap and (**c**,**d**) represent half of the face is shown. As shown in Figure 12, 25.16% of faces are not recognized. The reasons for not recognizing people have varied over the course of our monitoring of the performance of the experience. It is observed that several reasons prevent the model from identifying the person.