Efficient Facial Emotion Recognition Using An Optimized Deep Learning Model Based On Quantum Gazelle Optimization Algorithm
Olfa Askri, Ghaith Manita, Mohamed Ali Hajjaji · Procedia Computer Science · 2024
This study proposes a new approach for the Facial Expression Recognition (FER) system that combines the Quantum Gazelle Optimization Algorithm (QGOA), local binary patterns (LBP), and Histograms of Oriented Gradients (HOG) with an optimized Deep Neural Network (DNN) classifier. The system uses computer vision techniques and deep learning algorithms to identify emotions in facial expressions. The proposed technique first employs the HOG and LBP descriptors, crucial components with excellent pattern recognition capabilities. These descriptors provide features resilient to small local changes in posture and lighting. However, they also generate unimportant and obtrusive characteristics that hinder classification performance. The proposed approach uses a wrapper-based feature selector called QGOA to solve this issue, which decreases the training complexity and improves recognition performance. QGOA takes advantage of the properties of quantum computing to regulate the diversity of face features and make proper selections using quantum measurements and Q-bit superstitious states. Finally, the optimized DNN detects facial emotions based on the selected features. The proposed approach was tested on the widely adopted FER2013 dataset. The results of the extensive analysis demonstrate the effectiveness of the proposed approach over state-of-the-art systems.