International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 07 Issue: 04 | Apr 2020 www.irjet.net p-ISSN: 2395-0072 © 2020, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 152 Emotionalizer: Face Emotion Detection System Sweety Malviya 1 , Mayuri Rathore 2 , Vanshika Parihar 3 , Twinkle Rathore 4 , Shreyansh Malvi 5 , Sanjay Kalamdhad 6 1-7 Dept. of Computer Science and Engineering, Shri Balaji Institute of Technology and Management, Betul, M.P ---------------------------------------------------------------------***---------------------------------------------------------------------- Abstract - This project is mainly focused on emotion recognition by face detection. Facial expression is key aspect of social interactions in various situations. We used synthetic happy, sad, angry, fearful, disgust faces determining the amount of geometric change required to recognize these emotions. Emotion is a part of a person's character that consists of their feelings as opposed to their thought that is the key point of emotionalizing and analyzing each and every emotion by software i.e. able to read emotions as well as our brains do. It is basically developed on python using machine learning. Key Words: Facial emotion detection, facial recognition, facial analysis. 1. INTRODUCTION Emotionalizer as the name combined from the word emotion analyzer and the project as well analyzes human emotion by matching them to specific features regarding that particular expressions. Face recognition is an important part of the capability of the human perception system and is a routine task for humans while building a similar computational model of face recognition. The computational model not only contributes to theoretical insights but also to many practical applications like automated crowd surveillance, access control, design of human-computer interface (HCI), content- based image database management, criminal identification and so on. The earliest work on face recognition can be traced back at least to the 1950s in psychology and to the 1960s in the engineering literature. Some of the earliest studies include work on facial expression emotions by Darwin. But research on automatic machine recognition of faces started in the 1970s and after the seminal work of Kanade. In 1995, a review paper gave a thorough survey of face recognition technology at that time. At that time, video- based face recognition was still in a nascent stage. During the past decades, face recognition has received increased attention and has advanced technology. Many commercial systems for still face recognition are now available. Recently, significant research efforts have been focused on video- based face modeling/tracking, recognition and system integration. New databases have been created and evaluations of recognition techniques using these databases have been carried out. Over the last few decades, lots of work is been done in face detection and recognition. A facial recognition system is a computer application capable of identifying or verifying a person from a digital image or a video frame from a video source. One of the ways to do this is by comparing selected facial features from the image and a facial database. Face detection is a computer technology being used in a variety of applications that identifies human faces in digital images. Face detection also refers to the psychological process by which humans locate and attend to faces in a visual scene. It is typically used in security systems and can be compared to other biometrics such as fingerprint or eye iris recognition systems. Recently, it has also become popular as a commercial identification and marketing tool. Face detection can be regarded as a specific case of object- class detection. In object-class detection, the task is to find the locations and sizes of all objects in an image that belongs to a given class. Examples include upper torsos, pedestrians, and cars. Face-detection algorithms focus on the detection of frontal human faces. It is analogous to image detection in which the image of a person is matched bit by bit. Image matches with the image stores in the database. Any facial feature changes in the database will invalidate the matching process. A reliable face-detection approach based on the genetic algorithm and the eigenfaces technique. 2. PROBLEM DEFINITION In the early few years, several papers have been published on face detection in the community which discusses different techniques like a neural network, edge detectors and many more. There is a good survey by Chellapa, Wilson, and Sirohey (1995) which tells about the trends of paper in face detection. Previously, many researchers and engineers have designed different purpose-specific and application-specific detectors. The main goal of this kind of classifiers was to achieve a very high detection rate along with a low computational cost. Few examples of different detectors are