Real Time Face Emotion Detection Using CNN

Machine Learning

Face emotion detection has evolved as a key field of study in pattern recognition as well as computer vision, and its applications have extended to encompass in AI,HCI, and security monitoring. Convolution neural network (CNN) is a deep learning architecture that can extract critical picture characteristics, and it has been demonstrated to outperform older approaches like Support Vector Machines (SVM) as well as Principle Component Analysis which is (PCA) in situations with considerable variations in shooting circumstances. In this paper, we provide an enhanced technique of facial expression detection using CNN, with the objective of categorising face pictures into seven distinct expressions. We built a novel CNN structure that employs convolution kernels to extract implicit features and max pooling to lower the dimensionality of the extracted features, customised to the unique properties of facial emotion identification. Along with face emotion detection, will provide the accuracy percentage of a particular emotion is also detected

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.