A Deep Quantum Convolutional Neural Network Based Facial Expression Recognition For Mental Health Analysis

Area of Science:

• Artificial Intelligence
• Quantum Computing
• Medical Imaging

Background:

• Facial expression analysis is crucial for diagnosing psychiatric conditions.
• Current methods for facial expression recognition face limitations in speed and accuracy.
• Integrating advanced computational techniques can enhance clinical practice.

Purpose of the Study:

• To develop an automated facial expression recognition system for psychiatric illness assessment.
• To investigate the efficacy of quantum deep learning in analyzing facial emotional expressiveness.
• To enhance clinical practice through improved emotion detection technology.

Main Methods:

• Proposed a five-step method involving image preprocessing and segmentation.
• Implemented a hybrid deep learning model combining classical and quantum convolutional layers.
• Utilized quantum variational circuits for efficient feature learning.
• Applied performance enhancement techniques: image augmentation, fine-tuning, normalization, and transfer learning.
• Fused classical and quantum deep learning model outputs for improved recognition.

Main Results:

• The quantum convolutional neural network approach achieved faster training times ([Formula: see text]) compared to classical models ([Formula: see text]).
• The system demonstrated improved performance on benchmark datasets (KDEF, SFEW 2.0, FER-2013).
• Fusion of classical and quantum models led to enhanced recognition accuracy.

Conclusions:

• The proposed quantum-enhanced facial expression recognition system offers a faster and more accurate method for emotion detection in clinical settings.
• This technology has the potential to significantly aid in the diagnosis and monitoring of psychiatric illnesses.
• The study highlights the promise of quantum computing in advancing medical image analysis and artificial intelligence in healthcare.