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

Sanoar Hossain, Saiyed Umer, Ranjeet Kumar Rout, Hasan Al Marzouqi

    Research output: Contribution to journalArticlepeer-review

    3 Scopus citations

    Abstract

    The purpose of this work is to analyze how new technologies can enhance clinical practice while also examining the physical traits of emotional expressiveness of face expression in a number of psychiatric illnesses. Hence, in this work, an automatic facial expression recognition system has been proposed that analyzes static, sequential, or video facial images from medical healthcare data to detect emotions in people's facial regions. The proposed method has been implemented in five steps. The first step is image preprocessing, where a facial region of interest has been segmented from the input image. The second component includes a classical deep feature representation and the quantum part that involves successive sets of quantum convolutional layers followed by random quantum variational circuits for feature learning. Here, the proposed system has attained a faster training approach using the proposed quantum convolutional neural network approach that takes mathbb {O}{(}text {log}{(}{n}{)}{)} time. In contrast, the classical convolutional neural network models have mathbb {O}{(}{n}{{2}}{)} time. Additionally, some performance improvement techniques, such as image augmentation, fine-tuning, matrix normalization, and transfer learning methods, have been applied to the recognition system. Finally, the scores due to classical and quantum deep learning models are fused to improve the performance of the proposed method. Extensive experimentation with Karolinska-directed emotional faces (KDEF), Static Facial Expressions in the Wild (SFEW 2.0), and Facial Expression Recognition 2013 (FER-2013) benchmark databases and compared with other state-of-the-art methods that show the improvement of the proposed system.

    Original languageBritish English
    Pages (from-to)1556-1565
    Number of pages10
    JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
    Volume32
    DOIs
    StatePublished - 2024

    Keywords

    • convolutional neural network
    • Facial expression
    • mental health conditions
    • quantum
    • quantum variational circuit
    • quanvolutional
    • recognition

    Fingerprint

    Dive into the research topics of 'A Deep Quantum Convolutional Neural Network Based Facial Expression Recognition For Mental Health Analysis'. Together they form a unique fingerprint.

    Cite this