Facial Expression Evaluation through Recurrent neural Network

Facial expression is one of the most challenging and perplexing techniques ever attempted in the image processing paradigm. Humans express the majority of their emotions through their facial expressions, which can be used for other purposes like as identifying a person's mood. One of the most potent types of nonverbal communication is facial expression. The facial expression can be quite effective in precisely assessing an individual's mood. Humans can quickly identify facial expressions, while computers find it harder to do so. The recognition of mood via facial expression is a significant problem in computer vision. The facial expression is also extremely tough and subtle, which has shown to be the most difficult aspect of detection. Using machine learning approaches, this research study develops an excellent facial expression detection approach for mood diagnosis and Beverage Temperature detection. To achieve mood identification and Beverage Temperature detection, the described methodology employs Recurrent Neural Networks and Decision Tree. The accuracy of the methodology has been confirmed by the experimental results.