OQCNN: optimal quantum convolutional neural network for classification of facial expression

• Published in: Neural computing & applications 2023-04, Vol.35 (12), p.9017-9033
• Main Authors: Sathya, T., Sudha, S.
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Artificial neural networks; Automation; Classification; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Datasets; Emotions; Face recognition; Image Processing and Computer Vision; Neural networks; Original Article; Particle swarm optimization; Probability and Statistics in Computer Science; Search algorithms; Social factors; Verbal communication
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-022-08161-w

Human emotions play a vital role in developing social interaction as well as interpersonal relationships. It is because humans express and convey their thoughts and feelings visually rather than using verbal communication. Recently, facial expression recognition is greatly adopted in the emerging human–computer interaction and human–robot interaction technologies. Although humans can identify facial emotions virtually, the reliable automated recognition of facial expressions becomes a complicated task. The main intention of this paper is to develop an automated facial expression recognition system. To attain the goal, in this work, an improved particle swarm optimization bat tracking search algorithm optimized quantum convolutional neural network (IPSOBSA-QCNN) approach is proposed to accurately identify and classify facial expressions such as fear, happy, disgust, sad, surprise, negative, positive, and dominated. Although classification performance is limited by hyperparameter tuning concerns, the QCNN system efficiently recognizes face expressions at a high processing speed. Therefore, the hyperparameters of the quantum convolutional neural network (QCNN) are optimized to enhance the classification accuracy using the improved particle swarm optimization backtracking search algorithm (IPSOBSA). The proposed IPSOBSA-QCNN approach is evaluated using three diverse datasets namely the real-world affective faces dataset, the Emotic dataset, and the facial expression comparison dataset. The effectiveness of the proposed approach is determined by comparing the proposed IPSOBSA-QCNN approach with other existing methods using different evaluation metrics. The experimental results display that the proposed IPSOBSA-QCNN approach achieves higher classification accuracy of about 98% for the real-world affective faces dataset, 97.5% for the Emotic dataset, and 97% for the facial expression comparison dataset.