• Nayan Bahirame
• Dr Satish Gujar

Facial expressions give non-verbal information to humans. Automatic facial expression reorganization is an important role in the first of the nineties. Classical machine learning approaches typically need a fancy feature extraction method and manufacture poor results. Here we apply Mobile NET classifier model for recognizing the emotions. We tend to conjointly apply different loss functions and coaching tricks to be told CNNs with a powerful classification power. The experimental results show that our planned networks beat out progressive ways on the well-known FERC-2013 dataset provided on the Kaggle face expression recognition competition. As compared to the winning model of this competition, the quantity of parameters in our planned networks intensively decreases, which accelerates the general performance speed and makes the planned networks well appropriate for the period system

Image Processing, Deep Neural Networks

" Emotion Recognition Using Image Processing and Deep Neural Networks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 6, page no.c423-c427, June-2022, Available :http://www.jetir.org/papers/JETIR2206249.pdf

" Emotion Recognition Using Image Processing and Deep Neural Networks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 6, page no. ppc423-c427, June-2022, Available at : http://www.jetir.org/papers/JETIR2206249.pdf