(untitled) (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
Now a day’s artificial intelligence (AI) has become most efficient and perfect algorithm for the non-linear data. In this paper we are developing Facial expression recognition system (FER) by using deep learning technique. Every person will be interested to know about the emotion of a person by his face gestures, to know the emotions, so many techniques are developed in the existing, and here we are using AFBN and GFBN from the DNN. In the AFBN the given image will be converted into LBP image after performing the pre-processing and then CNN will be applied to the output image. In GFBN the action units (AU) calculated when there is change in facial expressions. In this paper the proposed method mainly focuses on combining these two results by Softmax function to finding the error. The Top-2 error will be considered, and it is called as second highest emotion. Whenever to calculate all the emotions of a person there must be image with neutral emotions and it will be generated by using auto-encoder technique. After getting the data of neutral image we can easily get. The output results are compared with previous methods to know the efficiency of proposed method. For CK+ dataset we got 96% accuracy and for JAFFE dataset 91% accuracy. By using the DNN structure we are getting 1.5-3% improvement, when we compare to older methods.