## 3 Results  
Analysis of the video content estimates the emotional impact on the viewer while they are watching the video [21; 22; 23]. Image or video frames-based outliers are predicted during fixation distance calculations. Salient regions in an image or frame are merged for saliency detection through saliency prediction models [23]. Fixation distance measures the distance of fixation points on the screen. The X-axis of the fixation distance graphs plotted showed the different kinds of emotion, whereas the Y-axis showed the mean distance from the centre of the screen. The fixation distance measured from the centre of the screen for different emotional content or videos for all video frames shown in Figure 4 is the measurement of the viewer attention towards the scene. This difference shows that the pattern of eye movement differs in line with emotions; it can be higher for sad emotional movies followed by neutral ones and then happy movies. Fixation distance of first 125 frames was also calculated, and the result in Figure 5 showed a slight difference with the pattern of eye movement shifting slightly higher for happy emotions, followed by sad and then neutral. There seems to be a contrasting effect on the subjects watching happier and sad videos for the first 125 frames. However, neutral videos attract the same attention for all frames. However, these results could show that the subjects watching videos are more focused on watching the sad emotional videos fully, which could be due to the fact that by nature, humans perceive and recognize sad emotions more quickly as compared to happy emotions. Perceiving or feeling emotion is an important part of daily activities. These types of emotions usually provide a signal to our brain that conveys information about danger or pleasure in the surrounding environment. Measurement of the viewer attention towards the scene is justified by the fixation distance graphs (Figures 4 and 5), as these graphs were made in line with the recorded eye movement from the subjects that were seated 57 cm away from the screen for all the videos according to the database. Another reason behind the result could be that the sad movies have more salient locations than the movies for the other emotions. Figure 4: Fixation distance from the centre of the screen for different emotional content/videos (all frames). The GBVS model applied to the video frames generated different types of saliency maps to those of the GBVS map image, GBVS map overlaid image, Itti-Koch image, and Itti-Koch map overlaid image, as shown in Figure 6.  
These all maps were plotted with the default parameters. Each of the following images has its own quality and features. The GBVS model shows the Itti-Koch model normalization graphically. The GBVS maps have the ability to identify the most salient regions/location in an image, then the Itti-Koch model computes saliency maps representing the areas that are not sufficient for conveying the semantic meaning of the image [23, 24]. So, for all the saliency maps shown in Figure 6, GBVS map images for each of the video frames were chosen for saliency mean calculations and the generated graph also showed varying results as compared to a fixation distance graph. Figure 7 shows the highest saliency mean for happy videos for all frames of the GBVS Map, followed by for neutral and sad videos. The result shows that happy videos have the most salient locations, which attracts more human attention. As with the previous result for the fixation distance of the first 125 frames, the saliency means for first 125 frames of the GBVS map images for all the videos shown in Figure 8 shows the same result as the saliency means for all of the frames. Therefore, the selective frame for both the fixation distance and the saliency value calculation showed the greater emotional trigger of the happy videos overall, followed by the sad and then neutral videos.