Emotion Analysis by Deep Learning Methods using Convolutional Neural Network
Pelash Choudhary1, Shravan Vijay2, Sathya R3
1Pelash Choudhary, B.Tech, Department of CSE, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
2Shravan Vijay, B.Tech, Department of CSE, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
3Sathay R, M.Tech (Ph.D), Assistant Professor (O.G), Department of CSE, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
Manuscript received on 18 April 2019 | Revised Manuscript received on 25 April 2019 | Manuscript published on 30 April 2019 | PP: 1806-1810 | Volume-8 Issue-4, April 2019 | Retrieval Number: D6113048419/19©BEIESP
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© 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: In machine learning, CNN uses a variation of multilayer perceptron designed to use relatively little preprocessing compared to other image classification algorithms. This means that the network learns the filtering that was handengineered in other algorithms. This independence of human endeavors for feature design is a major advantage due to which it is used in this paper. In the context of machine vision, image recognition is the capability of software to identify objects in images. The algorithm is used to train the model from a data set of around 10000 images and 12 videos. The model will detect and recognize types of feelings through the person’s expression, such as anger, fear, happiness, sadness, and surprise. The model gives an accuracy of 67%. This provides a behavioral measure for the study of emotion, cognitive process and social interaction.
Keywords: Emotion Analysis, Convolutional Neural Network, Facial Recognition, Reinforced Learning
Scope of the Article: Deep Learning