• Vaibhav Hanumant Gosavi
• Snehal Vitthal Jadhav
• Shivam Kumar Singh
• Gayatri Londhe
• Monica Gunjal

The facial emotion recognition system assumes a lot of significance in this period since it can capture mortal behavior, heartstrings, intentions, etc. We propose a fast face detector using an effective architecture predicated on a hierarchical cascade of neural network ensembles with which we achieve enhanced discovery delicacy and effectiveness. We propose a way to form a neural network ensemble by using several neural network classifiers, each of which is specialized in a subregion in the face-pattern space. These classifiers round each other and, together, perform the discovery task. In this way, simpler and further effective ensembles used at earlier stages in the cascade are suitable to reject the maturity of nonface patterns in the image background.

CNN (Convolutional Neural Network), Haar classifier, Haar Cascade frontal face, Deep learning, HOG (Histogram of Oriented Gradients), R-CNN (Region-based Convolutional Neural Networks)

"Face Detection and Emotion Detection Using Deep learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 5, page no.i252-i257, May-2022, Available :http://www.jetir.org/papers/JETIR2205936.pdf

"Face Detection and Emotion Detection Using Deep learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 5, page no. ppi252-i257, May-2022, Available at : http://www.jetir.org/papers/JETIR2205936.pdf