Open AccessGeometric positions and optical flow based emotion detection using MLP and reduced dimensions
Author(s) -
Khan Gulraiz,
Siddiqi Aiman,
Ghani Khan Muhammad Usman,
Qayyum Wahla Samyan,
Samyan Sahar
Publication year - 2019
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2018.5728
Subject(s) - computer science , optical flow , artificial intelligence , computer vision , landmark , facial expression , facial action coding system , key (lock) , coding (social sciences) , canny edge detector , emotion recognition , pattern recognition (psychology) , edge detection , image (mathematics) , image processing , mathematics , statistics , computer security
Recent times have witnessed an exponential increase in multimedia specifically visual contents. Emotions are considered an essential part for extracting facial features, evaluating the expressions and as a result predicting the emotions of any person is a trending topic of the time. Based on still images and consecutive video frames, a methodology has been proposed to anticipate the emotions. Facial action coding system (FACS) standards are utilised in the development of an automated visual based emotion detection system worldwide. Employing FACS, the authors estimated facial muscle movement by computing 24 landmark points, 16 mutual distances between them and wrinkles caused due to changing expressions. Canny edge detection has been deployed to calculate the intensity of wrinkles. Geometric positions and optical flow are the key methods deployed in the implemented methodology. The methodology was evaluated on self‐generated, JAFFE dataset and EmotioNet.