[1]ZHU Bowen,WU Yongming,LIU Yang.Research on facial expression recognition method for bedridden patients[J].CAAI Transactions on Intelligent Systems,2026,21(4):952-962.[doi:10.11992/tis.202510015]
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Research on facial expression recognition method for bedridden patients

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 21 Number of periods: 2026 4 Page number: 952-962 Column: 学术论文—机器感知与模式识别 Public date: 2026-07-05
Title:
Research on facial expression recognition method for bedridden patients
Author(s):
ZHU Bowen;  WU Yongming;  LIU Yang
School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510006, China
Keywords:
bedridden patients; facial expression recognition; YOLOv11; feature fusion; deep learning; attention mechanism; feature fusion; image classification; RAF-DB dataset
CLC:
TP391.4
DOI:
10.11992/tis.202510015
Abstract:
To address the challenge that conventional facial recognition methods fail to accurately capture complex facial expression features and dynamics in bedridden patients with disabilities—who often present with facial stiffness, dull gaze, and flattened affect—this study proposes a YOLOv11-based facial expression recognition method specifically tailored for this patient population. This method is specifically designed with three hierarchical modules to collaboratively optimize detection accuracy: the efficient up-convolution block to address the deficiency of adaptability in feature capture, the lightweight feature fusion module to enhance multi-scale feature reuse, and the C2 position-sensitive attention-context-aware attention detection head module to improve robustness in complex occlusion scenarios. Evaluation of the proposed method demonstrated that on the public RAF-DB dataset, it achieved mean average precision (mAP) values of 87.83% for mAP50 and 87.83% for mAP50-95, representing improvements of 8.46% and 8.48% respectively, compared to YOLOv11, with a parameter count of only 2.8×106. On a self-constructed dataset of bedridden disabled patients, the method achieved mAP50 and mAP50-95 values of 87.21% and 56.52%, corresponding to increases of 3.21% and 2.67% over YOLOv11.The experimental results indicate that the proposed method enables more accurate facial expression recognition in bedridden disabled patients with disabilities, thereby contributing to intelligent care monitoring for this population.
References:
[1] GAYA-MOREY F X, BUADES-RUBIO J M, PALANQUE P, et al. Deep learning-based facial expression recognition for the elderly: a systematic review[EB/OL]. (2025-02-04)[2025-10-15]. https://arxiv.org/abs/2502.02618.
[2] 王信, 汪友生. 基于深度学习与传统机器学习的人脸表情识别综述[J]. 应用科技, 2018(1): 65-72 WANG Xin, WANG Yousheng. Facial expression recognition based on deep learning and traditional machine learning[J]. Applied science and technology, 2018(1): 65-72
[3] 陈微, 祁郑晴, 李雪, 等. 失能老人居家护理需求的研究进展[J]. 当代护士(中旬刊), 2022, 29(11): 1-5 CHEN Wei, QI Zhengqing, LI Xue, et al. Research progress on home care needs of disabled elderly people[J]. Modern nurse, 2022, 29(11): 1-5
[4] 李钦云, 宋岳涛. 老年长期照护的国内外现状和展望[J]. 实用老年医学, 2023, 37(1): 83-86 LI Qinyun, SONG Yuetao. Present situation and prospect of long-term care for the elderly at home and abroad[J]. Practical geriatrics, 2023, 37(1): 83-86
[5] GHIMIRE D, LEE J. Geometric feature-based facial expression recognition in image sequences using multi-class AdaBoost and support vector machines[J]. Sensors, 2013, 13(6): 7714-7734
[6] 辛静. 基于帧间灰度差的动态表情识别[D]. 天津: 天津大学, 2009. XIN Jing. Dynamic facial expression recognition based on the gray difference of frames[D]. Tianjin: Tianjin University, 2009.
[7] AVANIJA J, MADHAVI K R, SUNITHA G, et al. Facial expression recognition using convolutional neural network[C]//2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition. Hyderabad: IEEE, 2022: 1-7.
[8] JIANG Qiqi, PENG Xiwei, CHEN Hanyu, et al. Facial expression recognition based on residual network[C]//2022 41st Chinese Control Conference. Hefei: IEEE, 2022: 7000-7006.
[9] 黎克迅, 高治军. LFSepNet: 融合Transformer的照明和面部特征解耦人脸识别方法[J]. 计算机工程与应用, 2026, 62(4): 201-209 LI Kexun, GAO Zhijun. LFSepNet: face recognition method with illumination and facial feature decoupling based on transformer[J]. Computer engineering and applications, 2026, 62(4): 201-209
[10] MA Hui, LEI Sen, CELIK T, et al. FER-YOLO-mamba: facial expression detection and classification based on selective state space[EB/OL]. (2024-05-03)[2025-10-15]. https://arxiv.org/abs/2405.01828.
[11] CHEN Yangbo, PENG Chunyan. Occlusion-aware facial expression recognition with dynamic feature weighting and block loss optimization[C]//Proceedings of International Conference on Image, Vision and Intelligent Systems 2025. Singapore: Springer, 2026: 197-207.
[12] LI Shan, DENG Weihong, DU Junping. Reliable crowdsourcing and deep locality-preserving learning for expression recognition in the wild[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 2584-2593.
[13] SHARMA S, SHANMUGASUNDARAM K, RAMASAMY S K. FAREC: CNN based efficient face recognition technique using Dlib[C]//2016 International Conference on Advanced Communication Control and Computing Technologies. Ramanathapuram: IEEE, 2017: 192-195.
[14] RAHMAN M M, MUNIR M, MARCULESCU R. EMCAD: efficient multi-scale convolutional attention decoding for medical image segmentation[C]//2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2024: 11769-11779.
[15] QIN Danfeng, LEICHNER C, DELAKIS M, et al. MobileNetV4: universal models for the mobile ecosystem[M]//Computer Vision–ECCV 2024. Cham: Springer Nature Switzerland, 2024: 78-96.
[16] CAI Xinhao, LAI Qiuxia, WANG Yuwei, et al. Poly kernel inception network for remote sensing detection[C]//2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2024: 27706-27716.
[17] ASHISH V, NOAM S, NIKI P, et al. Attention is all you need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. Auckland: NIPS, 2017: 6000–6010.
[18] SANDLER M, HOWARD A, ZHU Menglong, et al. MobileNetV2: inverted residuals and linear bottlenecks[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 4510-4520.
[19] LIU Zhuang, MAO Hanzi, WU Chaoyuan, et al. A ConvNet for the 2020s[C]//2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans: IEEE, 2022: 11966-11976.
[20] MOU Xiangwei, XIE Liuping, SONG Yongfu, et al. Dynamic occlusion-aware facial expression recognition guided by AA-ViT[J]. Electronics, 2026, 15(4): 764
[21] LI Yong, ZENG Jiabei, SHAN Shiguang, et al. Occlusion aware facial expression recognition using CNN with attention mechanism[J]. IEEE transactions on image processing, 2019, 28(5): 2439-2450
[22] ZHAO Zengqun, LIU Qingshan, WANG Shanmin. Learning deep global multi-scale and local attention features for facial expression recognition in the wild[J]. IEEE transactions on image processing, 2021, 30: 6544-6556
[23] MA Hui, CELIK T, LI Hengchao. Lightweight attention convolutional neural network through network slimming for robust facial expression recognition[J]. Signal, image and video processing, 2021, 15(7): 1507-1515
[24] LIU Ping, LIN Yuewei, MENG Zibo, et al. Point adversarial self-mining: a simple method for facial expression recognition[J]. IEEE transactions on cybernetics, 2022, 52(12): 12649-12660
[25] LIU Hanwei, CAI Huiling, LIN Qingcheng, et al. Adaptive multilayer perceptual attention network for facial expression recognition[J]. IEEE transactions on circuits and systems for video technology, 2022, 32(9): 6253-6266
[26] CAI Jie, MENG Zibo, KHAN A S, et al. Probabilistic attribute tree structured convolutional neural networks for facial expression recognition in the wild[J]. IEEE transactions on affective computing, 2023, 14(3): 1927-1941
[27] LIU Yang, ZHANG Xingming, KAUTTONEN J, et al. Uncertain facial expression recognition via multi-task assisted correction[J]. IEEE transactions on multimedia, 2024, 26: 2531-2543
[28] ZHANG Ziyang, TIAN Xiang, ZHANG Yuan, et al. Enhanced discriminative global-local feature learning with priority for facial expression recognition[J]. Information sciences, 2023, 630: 370-384
[29] 侯海燕, 谭玉枚, 宋树祥, 等. 头部姿态鲁棒的面部表情识别[J]. 广西师范大学学报(自然科学版), 2024, 42(6): 126-137 HOU Haiyan, TAN Yumei, SONG Shuxiang, et al. Head pose-robust facial expression recognition[J]. Journal of Guangxi normal university (natural science edition), 2024, 42(6): 126-137
[30] TAO Huanjie, DUAN Qianyue. Hierarchical attention network with progressive feature fusion for facial expression recognition[J]. Neural networks, 2024, 170: 337-348
[31] ZHOU Haoliang, HUANG Shucheng, ZHANG Feifei, et al. CEPrompt: cross-modal emotion-aware prompting for facial expression recognition[J]. IEEE transactions on circuits and systems for video technology, 2024, 34(11): 11886-11899
[32] MAO Jiawei, XU Rui, YIN Xuesong, et al. POSTER++: a simpler and stronger facial expression recognition network[J]. Pattern recognition, 2025, 157: 110951
[33] 黎豊玮, 谭玉枚, 宋树祥, 等. 基于注意力引导的遮挡感知面部表情识别[J/OL]. 广西师范大学学报(自然科学版) (2025-07-28)[2025-10-15]. https://doi.org/10.16088/j.issn.1001-6600.2024120301. LI Fengwei, TAN Yumei, SONG Shuxiang, et al. Haiyingoocclusion-aware facial expression recognition based on attention guidance[J/OL]. Journal of Guangxi Normal University(natural science edition)(2025-07-28)[2025-10-15]. https://doi.org/10.16088/j.issn.1001-6600.2024120301.
[34] 党宏社, 孟饶辰, 高宛蓉. 基于双流特征交叉融合Efficient Transformer的人脸表情识别[J]. 计算机工程与应用, 2025, 61(15): 251-257 DANG Hongshe, MENG Raochen, GAO Wanrong. Facial expression recognition based on dual-stream feature cross-fusion efficient transformer[J]. Computer engineering and applications, 2025, 61(15): 251-257
[35] ZHOU Bolei, KHOSLA A, LAPEDRIZA A, et al. Learning deep features for discriminative localization[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 2921-2929.
[36] CHATTOPADHAY A, SARKAR A, HOWLADER P, et al. Grad-CAM++: generalized gradient-based visual explanations for deep convolutional networks[C]//2018 IEEE Winter Conference on Applications of Computer Vision. Lake Tahoe: IEEE, 2018: 839-847.
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