Open AccessRecognition of Human Facial Expressions Using DCT-DWT and Artificial Neural Network
Author(s) -
Wasan. Maddah Alaluosi
Publication year - 2021
Publication title -
iraqi journal of science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.152
H-Index - 4
eISSN - 2312-1637
pISSN - 0067-2904
DOI - 10.24996/ijs.2021.62.6.34
Subject(s) - sadness , artificial intelligence , computer science , discrete cosine transform , facial expression , pattern recognition (psychology) , discrete wavelet transform , surprise , artificial neural network , preprocessor , feature (linguistics) , feature extraction , speech recognition , computer vision , wavelet transform , image (mathematics) , psychology , wavelet , anger , communication , linguistics , philosophy , psychiatry
Facial expressions are a term that expresses a group of movements of the facial fore muscles that is related to one's own human emotions. Human–computer interaction (HCI) has been considered as one of the most attractive and fastest-growing elds. Adding emotional expression’s recognition to expect the users’ feelings and emotional state can drastically improves HCI. This paper aims to demonstrate the three most important facial expressions (happiness, sadness, and surprise). It contains three stages; first, the preprocessing stage was performed to enhance the facial images. Second, the feature extraction stage depended on Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) methods. Third, the recognition stage was applied using an artificial neural network, known as Back Propagation Neural Network (BPNN), on database images from Cohen-Kanade. The method was shown to be very efficient, where the total rate of recognition of the three facial expressions was 92.9%.