• Redrouthu Bhagyasagar
• G Appa Rao

Emotion is a key topic in a variety of professions, including biomedical engineering, psychology, neuroscience, and mental health. This component of emotion recognition is crucial, because it is commonly used in the diagnosis of human brain and psychiatric diseases. Deep learning has gotten a lot of users' interest in the field of picture categorization, according to a recent poll. These emotions are employed not just for brain diagnosis, but also as a recommendation system to help consumers select goods that meet their requirements and preferences. This inspired us to create a system that can accurately and efficiently discern emotions based on the user's facial expressions. In this proposal, we aim to create an application that can be used to anticipate expressions in both still and moving photographs. Then compare the results of the CNN with the recurrent neural network (RNN) model. Once the image is taken from the video sequences, the system uses HAAR cascade to detect faces, crops the image, resizes it to the necessary dimension, and sends it to the model for prediction. Seven probability values will be generated by the model, matching to seven expressions. We compare the two models to see which one provides better face expression detection accuracy for the image dataset.

Deep Learning Neural Networks ,CNN, RNN,HAAR Cascade .

"Facial Expression Recognition Using Features Extraction Based On Cnn & Rnn Models", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 5, page no.c779-c785, May-2022, Available :http://www.jetir.org/papers/JETIR2205401.pdf

"Facial Expression Recognition Using Features Extraction Based On Cnn & Rnn Models", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 5, page no. ppc779-c785, May-2022, Available at : http://www.jetir.org/papers/JETIR2205401.pdf