International Journal of Trend in Scientific Research and Development (IJTSRD) Volume 3 Issue 5, August 2019 Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470 @ IJTSRD | Unique Paper ID – IJTSRD25217 | Volume – 3 | Issue – 5 | July - August 2019 Page 101 Human Emotion Recognition using Machine Learning Prof. Mrs. Dhanamma Jagli 1 , Ms. Pooja Shetty 2 1 Professor, 2 Student 1,2 MCA, Vivekanand Education Society’s Institute of Technology, University of Mumbai, Maharashtra, India How to cite this paper: Prof. Mrs. Dhanamma Jagli | Ms. Pooja Shetty "Human Emotion Recognition using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456- 6470, Volume-3 | Issue-5, August 2019, pp.101-103, https://doi.org/10.31142/ijtsrd25217 Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by /4.0) ABSTRACT It is quite interesting to recognize the human emotions in the field of machine learning. Using a person’s facial expression one can know his emotions or what the person wants to express. But at the same time it’s not easy to recognize one’s emotion easily its quite challenging at times. Facial expression consist of various human emotions such as sad, happy , excited, angry, frustrated and surprise. Few years back Natural language processing was used to detect the sentiment from the text and then it took a step forward towards emotion detection. Sentiments can be positive, negative or neutral where as emotions are more refined categories. There are many techniques used to recognize emotions. This paper provides a review of research work carried out and published in the field of human emotion recognition and various techniques used for human emotions recognition. KEYWORDS: deep-learning, deep-neural-networks, Tensor Flow, Inception, transfer learning, convolutional neural networks. I. INTRODUCTION The human face conveys an intricate blend of information including age, gender, ethnicity, identity, personality, intentions, and emotions. In addition, speech articulation greatly affects the facial appearance Facial expressions are a form of nonverbal communication. Any human gestures can be identified by observing the different movements of mouth, nose, eyes and hands. In this proposed system it is focusing on the human face for recognizing expression using machine learning. There are most of the datasets which are labelled as Valence – Arousal scores to capture emotion. Five years back training classifiers would have been used to make emotion word list, deciding what features to use to classify and then train SVM. However these features are becoming past due to Deep Learning, which can do feature extractions automatically, this is how we can built our Emotion Classifier at Parallel Dots. Deep Learning makes it easier by converting the problem into classification problem by identifying what exactly you want to predict. This vision of the future motivates the research for automatic recognizing of nonverbal actions and expression. Human emotion recognition has increased the attention in computer vision, pattern recognition, and human-computer interaction research communities. While having face-to-face conversion it is easy to identify the facial expression of a human being like blink rate can reveal how nervous or at ease a person may be. Raised eyebrows combined with a slightly forward head tilt indicate what is being expressed is a yes or no question. Lowered eyebrows are used for what was the questions . People use the muscles around the mouth area for talking and eating, and especially speech articulation. But using machine learning Emotion we have to create a dataset of emotions which is then fed to the neural network and trained accordingly. Reorganization is considered to be a key requirement in many applications such as affective computing technologies, intelligent learning systems, Biometrics, Facial recognition systems, video surveillance, Human computer interface , patient wellness monitoring systems, etc. Human emotion varies from person to person. Therefore human emotion detection is more challenging task in computer vision. Therefore reliable human emotion detection is required for the success of these applications. II. CHOICE OF NEURAL NETWORK There are multiple options for implementing the algorithm. Convolutional Neural Networks(CNN) and Recurrent Neural Network(RNN) are the two options any Data Scientist will have while solving the text classification problem. RNN is used for longer context and Convents is used for feature detection task. Neural Network is trained until we reach a creditable accuracy. III. IMPLEMENTATION III.I. Dataset Given an image/picture, detecting the human face is a complex task due to the possible variations of the face. The various shapes, angles and different poses that human face might have within the image cause such variation. The dataset contains a picture of human facial expressions of emotion. This material was developed in 1998 by Daniel Lindquist, Anders Flykt and Professor Arne Ohman at Karolinska Institute, Department of Clinical Neuroscience, Section of Psychology, Stockholm, Sweden. III.II. Tensor Flow Tensor Flow is an open source library for machine learning which is written in Python and C++. Tensor Flow is IJTSRD25217