• Shivani Patidar
• Pradeep Baniya

The importance of facial expression recognition in different applications. To do such task the traditional feature extraction is used which involves in complex processing. Previously various deep neural networks have been used for this task, as well it can be replaced by some improved methods. So, in this paper we have proposed a system for face recognition which uses the convolutional neural network (CNN) and KNN along with the detection of the edges of image. The proposed work flows in two steps in the first the normalization of the facial expression in the image is done, secondly the convolution is done for the extraction of the edges in images. After this the maximum pooling method is used for the dimensionality reduction. At the end the classification of the facial expression is done and the Softmax classifier is used for this classification and the face expression is recognized. The facial expression recognition experiment is done on the Fer-2013 dataset. The results obtained form the proposed approach gets a face recognition rate up to 92.45% for the used dataset. The given method works with lesser number of iterations for the recognition and the system provides approx 1.5 times fast execution as compared to the SDSRN algorithm.

Facial Expression Recognition, Image Edge Detection, Convolutional Neural Network, Deep Learning, Maximum Pooling Classification.

"Facial Expression Recognition using Convolutional Neural Network and k-Nearest Neighbors ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 7, page no.c212-c215, July-2021, Available :http://www.jetir.org/papers/JETIR2107297.pdf

"Facial Expression Recognition using Convolutional Neural Network and k-Nearest Neighbors ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 7, page no. ppc212-c215, July-2021, Available at : http://www.jetir.org/papers/JETIR2107297.pdf