Multi-Level Fusion for Facial Expression Recognition in Human Behavior Identification
Aqeel Hussein1, Ibraheem H.M.2 Sarah Ali Abdulkareem3, Ryam Ali Zubaid4, Noor Thamer5
1Department of Medical device technology Engineering, Alfarahidi University, Baghdad, Iraq
2 Computer Communications Engineering Department, Alrafidain University College, Baghdad, Iraq
3 Department of computer engineering techniques, Al-turath University College, Baghdad, Iraq
4 Department of computer engineering techniques, Mazaya University college, Thi Qar, Iraq
5 Accounting Department, Al-Mustaqbal University College , 51001 Hillah, Babylon , Iraq
Emails:;aqeel.hussien@alfarahidiuc.edu.iq; ibraheem.hatem.elc@ruc.edu.iq; sarah.ali@turath.edu.iq; noorthamer2020@mustaqbal-college.edu.iq;
Abstract
In this study, we present a multi-level fusion of deep learning technique for facial expression identification, with applications spanning the fields of cognitive science, personality development, and the detection and diagnosis of mental health disorders in humans. The suggested approach, named Deep Learning aided Hybridized Face Expression Recognition system (DLFERS), classifies human behavior from a single image frame through the use of feature extraction and a support vector machine. An information classification algorithm is incorporated into the methodology to generate a new fused image consisting of two integrated blocks of eyes and mouth, which are very sensitive to changes in human expression and relevant for interpreting emotional expressions. The Transformation of Invariant Structural Features (TISF) and the Transformation of Invariant Powerful Movement (TIPM) are utilized to extract features in the suggested method's Storage Pack of Features (SPOF). Multiple datasets are used to compare the effectiveness of different neural network algorithms for learning facial expressions. The study's major findings show that the suggested DLFERS approach achieves an overall classification accuracy of 93.96 percent and successfully displays a user's genuine emotions during common computer-based tasks.
Keywords: Artificial Intelligence, Facial Expressions; Deep fusion features; Smart Decision System; Behavior traits.