An Efficient Facial Expression Recognition System Using Novel Supervised Machine Learning by Comparing CNN over Google Net

Abstract

Aim: The main aim of this research article is classification of facial expression recognition with improved accuracy by using
Convolutional Neural Network(CNN) comparison with GoogleNet. Materials and Methods: The data of the facial expressions is taken from the FER2013 available on kaggle.The most widely utilized technique for accurately assessing photos is the convolutional neural network (CNN), and GoogleNet is also employed here to compare the accuracy of Novel image classification. Results: The Convolution neural network(CNN) produces 82.14% accuracy in predicting facial expressions on the dataset,whereas GoogleNet produces 75.09% accuracy. Convolutional neural network(CNN) is better than GoogleNet.Between the study groups, there is a statistically significant difference (p<0.05). Conclusion: Convolutional Neural Network provides better outcomes in accuracy rate when compared to GoogleNet for predicting facial expressions.