An Enhanced Framework for Visual Attention on Human Face using Artificial Intelligence
Faces of human beings have been the main target of visual attention since faces anticipate lot of statistics. Certain visual attention models including facial signs are more efficient in scenes accommodating faces. No visual attention model is especially created for human face. On faces, various higher aspects have impact on visual attention distribution. In general, there are several communication models where faces contain scenes, such as video calls. Specific visual attention model which is created for face are of great value in such circumstances. Data related to eye movement show that comprehensive visual attention allotment on faces also exist. Utilizing face identification and facial landmark localization, we discover that some facial expression are highly beneficial for visual attention prediction. The execution of the many visual attention models may be refined by including such facial expression.