Panliang Mu, Madaan Sanjay, Babikir Ali Siddiq Ahmed, J Gowrishankar, Khatibi Ali, Alsoud Anas Ratib, Mittal Vikas, Kumar Lalit, Santhosh A Johnson
National Key Laboratory of Fundamental Science on Synthetic Vision, College of Computer Science, Sichuan University, Chengdu, 610017, China.
Department of Computer Engineering and Applications, GLA University, Mathura, 281001, Uttar Pradesh, India.
Sci Rep. 2025 Feb 7;15(1):4665. doi: 10.1038/s41598-025-85206-9.
Facial expression recognition (FER) has advanced applications in various disciplines, including computer vision, Internet of Things, and artificial intelligence, supporting diverse domains such as medical escort services, learning analysis, fatigue detection, and human-computer interaction. The accuracy of these systems is of utmost concern and depends on effective feature selection, which directly impacts their ability to accurately detect facial expressions across various poses. This research proposes a new hybrid approach called QIFABC (Hybrid Quantum-Inspired Firefly and Artificial Bee Colony Algorithm), which combines the Quantum-Inspired Firefly Algorithm (QIFA) with the Artificial Bee Colony (ABC) method to enhance feature selection for a multi-pose facial expression recognition system. The proposed algorithm uses the attributes of both the QIFA and ABC algorithms to enhance search space exploration, thereby improving the robustness of features in FER. The firefly agents initially move toward the brightest firefly until identified, then search transition to the ABC algorithm, targeting positions with the highest nectar quality. In order to evaluate the efficacy of the proposed QIFABC algorithm, feature selection is also conducted using QIFA, FA, and ABC algorithms. The evaluated features are utilized for classifying face expressions by utilizing the deep neural network model, ResNet-50. The presented FER system has been tested using multi-pose facial expression benchmark datasets, including RaF (Radboud Faces) and KDEF (Karolinska Directed Emotional Faces). Experimental results show that the proposed QIFABC with ResNet50 method achieves an accuracy of 98.93%, 94.11%, and 91.79% for front, diagonal, and profile poses on the RaF dataset, respectively, and 98.47%, 93.88%, and 91.58% on the KDEF dataset.
面部表情识别（FER）在包括计算机视觉、物联网和人工智能在内的各个学科中都有先进的应用，支持医疗护送服务、学习分析、疲劳检测和人机交互等不同领域。这些系统的准确性至关重要，并且取决于有效的特征选择，这直接影响它们在各种姿势下准确检测面部表情的能力。本研究提出了一种名为QIFABC（混合量子启发萤火虫和人工蜂群算法）的新混合方法，该方法将量子启发萤火虫算法（QIFA）与人工蜂群（ABC）方法相结合，以增强多姿势面部表情识别系统的特征选择。所提出的算法利用QIFA和ABC算法的属性来加强搜索空间探索，从而提高FER中特征的鲁棒性。萤火虫代理最初朝着最亮的萤火虫移动，直到被识别，然后搜索过渡到ABC算法，目标是花蜜质量最高的位置。为了评估所提出的QIFABC算法的有效性，还使用QIFA、FA和ABC算法进行了特征选择。通过利用深度神经网络模型ResNet-50，将评估后的特征用于对面部表情进行分类。所提出的FER系统已使用多姿势面部表情基准数据集进行测试，包括RaF（拉德堡德人脸）和KDEF（卡罗林斯卡定向情感人脸）。实验结果表明，所提出的QIFABC与ResNet50方法在RaF数据集上，正面、对角和侧面姿势的准确率分别达到98.93%、94.11%和91.79%，在KDEF数据集上分别为98.47%、93.88%和91.58%。
