Emotion Recognition using Convolutional Neural Network
Praveen.R1, Benjula Anbu Malar M.B2
1R.Praveen, pursuing Master Of Computer Application, Vellore Instiute Of Technology, Vellore.
2Prof Benjula Anbu Malar.M.B, Assistant professor, Vellore Instiute Of Technology, Vellore.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 20, 2020. | Manuscript published on March 30, 2020. | PP: 1748-1765 | Volume-8 Issue-6, March 2020. | Retrieval Number: F7921038620/2020©BEIESP | DOI: 10.35940/ijrte.F7921.038620
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Emotion recognition system place the important role in many fields, particularly image processing, medical science, machine learning. As per human needs, the effect and potential use of programmed emotion recognition have been developing in a wide scope of utilizations, including human-PC communication, robot control and driver state observation. In any case, to date, vigorous acknowledgment of outward appearances from pictures and recordings is yet a testing errand because of the trouble in precisely extricating the helpful passionate highlights. These highlights are regularly spoken to in various structures, for example, static, dynamic, point-based geometric or area based appearance. Facial development highlights, which incorporate component position and shape changes, are by and large brought about by the developments of facial components and muscles on the face of enthusiastic manner. Emotion recognition system has many applications. and it plays a vital part in fault detection and in gaming application. In this project the emotion recognition is of dynamic way and not like uploading the image and finding the emotion. And this is achieved with the help of the concept of machine learning called Convolutional Neural Network. This is one of the most familiar deep learning concept. The main moto of using this concept is to maintain accuracy. The CNN consists of many intermediate state which plays the important role in producing the accurate output. The layers of CNN are input layer, hidden layer and output layer. The hidden layer is used to update weight, bias and activation function. If we use the CNN methodology the unwanted parts which is un necessary for the emotion recognition will be eliminated accurately. The CNN helps to reduce our elimination task in easier way and with minimal steps.
Keywords: Activation Function, Bias, Conv 2D, Convolutional Neural Network, Max pooling, Normalization, Weights. HR classifier.
Scope of the Article: Pattern Recognition.