An Efficient Facial Expression Recognition System Using Novel Image Classification by Comparing CNN over Res Net (Journal of Pharmaceutical Negative Results, 2022)
Aim: The aim of this research article is classification of facial expression recognition Convolutional Neural Network (CNN)compared with ResNet yielded better accuracy. Materials and Methods: The data of the facial expressions is taken from the FER2013 available on kaggle.The convolutional neural network (CNN) is the most widely used algorithm for image analysis with best accuracy and ResNet is also an algorithm used 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 ResNet produces 78.01% accuracy. Convolutional neural network (CNN) is better than ResNet. With (p<0.05), there is a statistically significant difference between the research groups. Conclusion : Convolutional Neural Network provides better outcomes in accuracy rate when compared to ResNet forpredicting facial expressions.