Open AccessA Standalone Vision Device to Recognize Facial Landmarks and Smile in Real Time Using Raspberry Pi and Sensor
Author(s) -
Navjot Rathour,
Anita Gehlot,
Anita Gehlot
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.f8957.088619
Subject(s) - computer science , artificial intelligence , landmark , facial expression , face (sociological concept) , support vector machine , computer vision , feature extraction , field (mathematics) , raspberry pi , face detection , feature (linguistics) , task (project management) , identification (biology) , curiosity , emotion detection , emotion classification , facial recognition system , emotion recognition , psychology , engineering , social science , philosophy , mathematics , systems engineering , linguistics , sociology , biology , embedded system , social psychology , botany , pure mathematics , internet of things
In current scenario of technological advancement, human-machine association is becoming sought after and machine needs to comprehend human emotions and feelings. The productivity of an exercise can be improved to a considerable extent, if a machine can distinguish human feelings by understanding the human conduct. Feelings can comprehend by content, vocal, verbal and outward appearances. The major deciding factor in the identification of human emotions is Facial expression. Working with facial images and emotion is real time is a big task. It is also found that confined amount of work has been done in this field. In this paper, we propose a technique for facial landmark detection and feature extraction which is the most crucial prerequisite for emotion recognition system by capturing the facial images in real time. The proposed system is divided into three tightly coupled stages of face detection, landmark detection and feature extraction. This is done by HOG and Linear SVM-based face detector using dlib and OpenCV. The curiosity of our proposed strategy lies in the execution stage. Raspberry Pi III, B+ and a normal exactness of 99.9% is accomplished at ongoing. This paper can be proved as the basis of real time emotion recognition in majority of applications.