Integration of Facial Emotion Recognition and Deep Learning for Resolving Traffic Flow Issue

Facial Emotion Recognition (FER) and deep learning has shown huge potential to improve security and accessibility in public service. In India, traffic flow issues are a serious concern for security and feasibility in customer accessibility. Overcrowding and poor infrastructure lead to traffic congestion and are further exacerbated by the unregulated vehicle population, lack of skilled drivers and knowledge of traffic rules, and also lack of enforcement of emergency services. Integration of FER and deep learning can resolve this problem for now as FER detects emotions like of drowsiness, aggression, and distraction whereas deep learning made predictions about traffic patterns, lane detection and real time congestion level, also we work on its scalability, real time accuracy, cross culture and cross ethnic aspect and integration with new upcoming technology. Also looking at the more human aspect of it as we can also use it in detecting trafficking. But it is important to note that it is effective technology, but it is not a silver bullet and with this type of complex model to work on we need to be more dependent upon data and architecture models based on new technology. This all should be done in coordination with existing, upcoming technology and strategies.