Open AccessFacial expression recognition using a combination of enhanced local binary pattern and pyramid histogram of oriented gradients features extraction
Author(s) -
Sharifnejad Maede,
Shahbahrami Asadollah,
Akoushideh Alireza,
Hassanpour Reza Zare
Publication year - 2020
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/ipr2.12037
Subject(s) - artificial intelligence , local binary patterns , pattern recognition (psychology) , computer science , histogram , feature extraction , histogram of oriented gradients , chin , facial expression , face (sociological concept) , feature (linguistics) , pyramid (geometry) , computer vision , support vector machine , facial recognition system , three dimensional face recognition , image (mathematics) , face detection , mathematics , medicine , social science , linguistics , philosophy , sociology , anatomy , geometry
Abstract Automatic facial expression recognition, which has many applications such as drivers, patients, and criminals' emotions recognition, is a challenging task. This is due to the variety of individuals and facial expression variability in different conditions, for instance, gender, race, colour and changing illumination. In addition, there are many regions in a face image such as forehead, mouth, eyes, eyebrows, nose, cheeks and chin, and extracting features of all these regions are expensive in terms of computational time. Each of the six basic emotions of anger, disgust, fear, happiness, sadness and surprise affect some regions more than the other regions. The goal of this study is to evaluate the performance of enhanced local binary pattern, pyramid histogram of oriented gradients feature‐extraction algorithms and their combination in terms of recognition accuracy, feature vector length and computational time on one, two and three combined regions of a face image. Our experimental results show that the combination of both feature‐extraction algorithms yields an average recognition accuracy of 95.33% using three regions, that is, the mouth, nose and eyes on Cohn–Kanade dataset. Besides, the mouth region is the most important part in terms of accuracy in comparison to eyes, nose and combination of both eyes and nose regions.