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Article(id=1322168016402498552, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322167989802217791, articleNumber=null, orderNo=null, doi=10.12102/j.issn.1009-6493.2026.04.020, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1736697600000, receivedDateStr=2025-01-13, revisedDate=1769443200000, revisedDateStr=2026-01-27, acceptedDate=null, acceptedDateStr=null, onlineDate=1793159019804, onlineDateStr=2026-10-28, pubDate=1771948800000, pubDateStr=2026-02-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793159019804, onlineIssueDateStr=2026-10-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793159019804, creator=13701087609, updateTime=1793159019804, updator=13701087609, issue=Issue{id=1322167989802217791, tenantId=1146029695717560320, journalId=1220038315760373764, year='2026', volume='40', issue='4', pageStart='529', pageEnd='704', issueExtLink='null', onlineDate='null', pubDate='1771948800000', pubDateStr='2026-02-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793159013461, creator='13701087609', updateTime=1793159013461, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=663, endPage=667, ext={EN=ArticleExt(id=1322168016628990969, articleId=1322168016402498552, tenantId=1146029695717560320, journalId=1220038315760373764, language=EN, title=Application progress of facial recognition technology in cognitive impairment screening for the elderly, columnId=1220689485315101342, journalTitle=Chinese Nursing Research, columnName=Review Articles, runingTitle=null, highlight=null, articleAbstract=

This paper reviewed the screening methods of facial recognition technology for cognitive impairment in the elderly,discussed the existing problems and solutions of this technology,so as to provide new ideas for the application of facial recognition technology in the screening of elderly patients with cognitive impairment in China.

, authors=Xiaotong WANG1, 2, 3, Hui HU1, 2, 3, authorsList=Xiaotong WANG, Hui HU, authorCompany=null, correspAuthors=Hui HU, authorNote=null, correspAuthorsNote=
HU Hui， E-mail：
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综述面部识别技术在老年人认知障碍中的筛查方式，探讨了该技术目前存在的问题及解决方法，以期为面部识别技术在我国老年认知障碍病人筛查中的应用提供新思路。

, authors=汪小彤1, 2, 3, 胡慧1, 2, 3, *, *, authorsList=汪小彤, 胡慧, authorCompany=null, correspAuthors=胡慧, authorNote=

汪小彤，硕士研究生在读

, correspAuthorsNote=
胡慧，E-mail:
HU Hui， E-mail：
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面部识别技术在老年人认知障碍筛查中的应用进展
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汪小彤 1, 2, 3 , 胡慧 1, 2, 3, *, *
护理研究 | 科研综述 2026,40(4): 663-667
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护理研究 |科研综述 2026 , 40 (4) : 663 -667
面部识别技术在老年人认知障碍筛查中的应用进展
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汪小彤1, 2, 3, 胡慧1, 2, 3, *, *
作者信息
  • 1湖北中医药大学护理学院，湖北 430065
  • 2湖北中医药大学附属医院（湖北省中医院）
  • 3湖北时珍实验室
通讯作者:
胡慧，E-mail:
HU Hui， E-mail：
作者简介:

汪小彤，硕士研究生在读

Application progress of facial recognition technology in cognitive impairment screening for the elderly
Xiaotong WANG1, 2, 3, Hui HU1, 2, 3
Affiliations
  • 1School of Nursing, Hubei University of Chinese Medicine, Hubei 430065 China
  • 2Affiliated Hospital of Hubei University of Chinese Medicine(Hubei Provincial Hospital of Traditional Chinese Medicine)
  • 3Hubei Shizhen Laboratory
    • Corresponding Author:
    HU Hui， E-mail：
出版时间: 2026-02-25 doi: 10.12102/j.issn.1009-6493.2026.04.020
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摘要
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综述面部识别技术在老年人认知障碍中的筛查方式，探讨了该技术目前存在的问题及解决方法，以期为面部识别技术在我国老年认知障碍病人筛查中的应用提供新思路。

老年人  /  认知障碍  /  面部识别  /  综述

This paper reviewed the screening methods of facial recognition technology for cognitive impairment in the elderly,discussed the existing problems and solutions of this technology,so as to provide new ideas for the application of facial recognition technology in the screening of elderly patients with cognitive impairment in China.

the elderly  /  cognitive impairment  /  facial recognition  /  review
汪小彤, 胡慧. 面部识别技术在老年人认知障碍筛查中的应用进展. 护理研究, 2026 , 40 (4) : 663 -667 . DOI: 10.12102/j.issn.1009-6493.2026.04.020
Xiaotong WANG, Hui HU. Application progress of facial recognition technology in cognitive impairment screening for the elderly[J]. Chinese Nursing Research, 2026 , 40 (4) : 663 -667 . DOI: 10.12102/j.issn.1009-6493.2026.04.020
正文
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有研究预测，2030年阿尔茨海默病及阿尔茨海默病相关痴呆（Alzheimer's disease and Alzheimer's disease-related dementias,AD/ADRD）的社会年度总成本将超过30 000亿元，到2050年将达到117 700亿元，给全球公共卫生和社会护理带来的重大挑战[1]。阿尔茨海默病通常始于轻度认知障碍（mild cognitive impairment,MCI），其表现包括早发性记忆丧失、认知能力下降以及口头语言和视觉/空间感知障碍[2]。我国12%~50%的60岁及以上人群患有轻度认知障碍，其中10%~20%的病人可能会在1年内发展为痴呆[3]，疾病早期筛查非常重要。然而，目前针对轻度认知障碍的许多诊断方法都是侵入性的或耗时的[4-5]，大部分研究还是采用常规的认知问卷筛查或者是通过问卷数字化的平台进行筛查[4]。此外，在研究主题方面，既往研究十分关注识别认知障碍的早期迹象，近年来，随着机器学习技术的发展，快速筛查认知障碍病人的研究也逐渐引发关注。因此，需要一种简单、非侵入性且廉价的筛查方法。面部表情作为人类表达情感状态和意图的一种有力、自然、普遍的信号之一，不仅反映了情绪的即时变化，还可能揭示大脑功能和认知状态的细微变化[6-7]。本研究通过阐述面部识别技术在老年人认知障碍筛查中的应用现状，以期为其后续研究提供一定的理论基础。
1　情绪图片
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1.1　纸质化图像识别
Ekman等[8]通过跨文化研究确定了愤怒、厌恶、恐惧、快乐、悲伤和惊讶6种基本情绪，并证明了不同种族人群对这些情绪的感知的高度一致性。随后，轻蔑被添加为第7种基本情绪[9]。面孔情绪识别测试（Facial Emotion Recognition Test，FERT）是目前国际上常用的面部情绪刺激方法[10]，包括国外学者设计的面孔表情图片集（Pictures of Facial Affect，POFA）[11]和国内学者开发的面部情绪测试[12]等。早期研究大多运用纸质化的情绪图片，侧重于调查受试者对几种基本情绪的准确识别率。Calvo等[13]发现，快乐面孔的得分高于其他基本表情，其次是惊讶、悲伤和愤怒，而厌恶和恐惧的得分最低，这与Amlerova等[14]研究得出的结论相似。这可能与额叶和颞叶等大脑区域与视觉和听觉情绪处理有关，与未受损者相比，认知障碍老年人在观看消极的情绪刺激时杏仁核激活减少。因此，特定大脑区域的变化可以解释识别快乐情绪比其他情绪更容易受到衰老影响的原因[15]。也有一些学者在情绪识别的结果方面提出一些不同的观点，如Hayashi等[16]对来自医院记忆诊所的187例受试者进行测试，测试照片共有4个人，每个人8张照片，共32张照片，每个人的8张照片中包括快乐、悲伤、愤怒和惊喜4种情绪，每种情绪都有2张不同的照片进行展示，面部表情使用变形方法进行改造，结果发现轻度认知障碍的受试者在识别快乐和惊喜这类积极情绪时，比识别消极情绪更加困难。究其原因可能是两项研究视角的不同，后者将认知功能障碍病人在面部情感识别划分为识别正面和负面情绪的评估，将负面情绪限制为悲伤和愤怒两种，其次是刺激量的不同（刺激的时间、观看图片的数量或图片线索等）及认知功能的评价指标导致[17]。建议后期调查中进一步规范刺激量及评价指标，同时进一步探究面部情绪识别与认知功能不同认知域之间的关系，以便在后期干预时可以专注于特定领域的认知训练[18]。此外，建议评估变量包括其情绪状态，如抑郁和焦虑，并严格控制其他个体差异，提升结论可信度[7]。
1.2　数字化图像识别
随着研究的深入以及数字化技术的不断开展，出现了一些新的数字化筛查手段，从图像中提取面部特征作为输入，输出预先设计的情感类别[19]。García-Casal等[20]开发了一种基于计算机的情绪识别测试，共测试了212名参与者，测试内容包括53个正式刺激和7个练习项目（每种情绪对应一个练习项目），指令通过大屏幕展示，并通过扬声器播放，为防止口头语言处理带来的干扰，还要求参与者将刺激图像与书面标签进行匹配，如果参与者在30 s内未作答，测试将自动跳转到下一个刺激，并记录为遗漏错误。结果表明该测试区分了健康老年人与阿尔茨海默病和轻度认知障碍病人，该测试不仅能够弥补评估量表在情绪识别方面的不足，还可以通过对准确性和处理速度的评估，提高量表在认知障碍筛查中的整体预测能力。但考虑到痴呆病人较正常对照组更难持续集中注意力，受试者在涉及区分多种负面情绪的复杂任务中可能表现较差，因此，在设计研究时需合理规划照片数量和测试时间，以减轻受试者的负担并提高数据质量。
2　动态视频
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2.1　观看视频中的情绪变化
现实生活中的互动往往依赖于快速非语言线索与复杂语言线索的交织，而其中面部情绪的识别在社交环境中的互动中起着至关重要的作用，能够根据不同情境调整行为[21]，而发生认知障碍者对于说话者的动态面部表情解读和反馈可能受损[22]。
Alonso-Recio等[23]开发一种基于情感特性和机器学习的新型自动分类工具，受试者只需要观看预先制定的情感电影，机器自动收集包括自我评估人体模型去评估情绪反应、皮肤电活动和面部表情在内的多维情绪数据。使用连续分解分析和 EomNet 提取情绪特征，双向长短期记忆网络用于训练分类模型，采用混合融合方法，结合早期特征融合与晚期决策融合，以优化模型性能。结果显示，在区分正常与可能阿尔茨海默病病人时模型表现最佳，受试者工作特征曲线下面积（AUC）为0.92，区分轻度认知障碍与可能的阿尔茨海默病病人之间的AUC为0.88，区分正常人与轻度认知障碍病人之间的AUC为0.82，总的来说，综合多种情绪特征能够有效区分认知正常、轻度认知障碍与阿尔茨海默病。
但杨晶晶等[24]研究指出，与认知健康的老年人相比，阿尔茨海默病病人在一定程度上表现出情感淡漠的特征，但其面部表情的丰富度却高于健康对照组。这与目前大部分研究结果不同，可能是由于使用这种筛查技术时研究者在提取的特征和使用的分类器方面通常有所不同以及过拟合问题的解决方法还有待完善[25]，面部筛查技术作为一种新兴方法，需要跨学科的技术与知识融合。目前，缺乏统一的报告规范和标准化的研究体系，且根据研究目的最终结果的报告标准存在差异[26]。此外，也可能是由于研究背景的不同，研究指出东方人在识别负面面部表情方面不如西方人准确，以及东方人比西方人更倾向于在面部表情中感知到复杂的情绪[27]。因此，未来应更关注文化背景对于面部情绪感知的研究，构建合理的研究方案和技术，以推动认知筛查技术的进一步发展。
2.2　观看视频后的情感解析
神经退行性过程可以逐渐改变情感反应并影响社交能力，导致轻度认知障碍病人的孤独感和问题恶化[28]。研究表明，与年轻人相比，老年人尤其是认知障碍者更加难以感知社交线索，解析视觉社会信息需要很高的认知负荷，而且认知衰退者可能无法控制自身注意力去观看不太相关的区域[29]。
Xu等[22]招募了82名受试者（38名年龄较大且出现认知衰退者，44名年轻人）在眼动仪上完成了社会认知评估电影的任务，系列视频的总时长为15.76 min，每个视频的平均时长为22 s。在每个视频之后，设计了1个多项选择题，每个问题提供4个备选答案，这些问题主要围绕演员的情感展开，重点关注复杂的心理状态，如一级和二级错误信念、欺骗、失礼行为的推断、说服技巧、隐喻、讽刺以及反语等。针对每个多项选择题，4个选择被归类为：正确答案；字面意义上正确或过于简单的心理状态推理，即“心理化不足”；夸大或过于复杂的心理状态推理，即“心理化过度”；未展现任何理解及证据。结果显示，与年轻人相比，老年人的总体准确性较低，同时使用主成分分析法分析眼睛凝视数据发现，年龄增长和注意力不集中与受试者的准确性降低有关，目前的已有研究也支持这个观点[30]。
但有一些研究提出了不同意见，他们认为社会个体终其一生都会持续积累社会知觉经验，社会交往相关的专门知识或技能也在不断增长，所以对于任务的生态效度增加时，认知衰退者也可以利用其不断积累的社会经验加工情绪信息[29,31]。结果差异的原因可能是：研究采用的任务选择广泛，使得很难比较和重叠获得的结果；这种异质性也可以被认为是一种优势，因为它使我们能够全面探索情绪识别领域，如流体智力和晶体智力对于老年人认知衰退的影响程度是否相同。建议未来研究在涉及的测试和刺激指标中应尽可能制定更为统一的标准，以便使结论更为客观。
3　半结构化访谈视频
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面部肌电图是一种复杂的测量工具，使研究人员通过面部肌肉发现细微的情绪成分，从而加深人们对面对面社交互动中出现的情绪相关现象的理解[32]。基于社会文化规范，人们有时会压抑他们的情绪感受和经历，这可能包括压制相关的面部表情[33]，但自我报告本质上是主观的，需要因人而异的内省能力，并且受到许多偏见和规范约束的影响。面部肌电图进一步使人们能够区分真实感受到的情绪和摆姿势的影响/情绪，并可以发现原本不会意识到的面部变化[28]。
Alsuhaibani等[34]基于I-CONECT临床试验（该试验围绕特定主题对研究对象进行了3轮、每轮30 min的半结构化访谈，并记录了老年受试者的互动视频，以评估其认知状态）开发了一个框架用于评估受试者的认知状况，采用卷积自动编码器提取面部空间特征，并在基线、干预第6个月和第12个月3个时间点进行访谈，结合转换器提取时间信息，结果显示，结合面部和交互特征的空间-时间注意力模块提高了认知状态预测的准确性。表明受试者的交互特征对于提高轻度认知障碍病人筛查的准确性至关重要。然而，该研究也指出，视频质量不仅受到传统技术因素（如房间照明、视角和面部外观）的影响，还与访谈过程中受试者的全脸显示情况相关。而受试者在视频的大部分时间里未能正面出镜，将会增加数据处理的挑战[35]。因此，建议访谈前调查员可以向老年受试者解释访谈的流程，告知他们摄像头的位置及其重要性，以及如何保持正面对着镜头；通过简短而明确的说明，减少老年人对设备操作的困惑。
4　混合多模态识别
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对情绪识别中与年龄相关的差异研究通常以单模形式呈现刺激（即仅面孔或仅声音），这可能会降低生态效度，并且可能无法反映人们在日常生活中如何感知情绪[36]。周滢[10]指出，与单模态和双模态特征融合分类模型相比，多模态特征融合分类模型在情绪问题的分类效果上表现更佳，当多模态信息可用时，与年龄相关的差异会大大降低。Kim等[37]小组开发的虚拟信息亭测试在其中记录了各种行为数据，例如手部和眼球运动，基于这些时间序列数据计算手部移动速度、注视持续时间的比例、完成时间和错误数4个行为特征，与健康对照组相比，轻度认知障碍病人手部移动速度较慢，注视时间比例较低，完成时间更长，会出现更多错误。有研究在社区中招募了1 481名年龄>60岁的老年人，采用智能眼动分析和运动功能定量系统对受试者进行了全方位的眼动和步态特征采集，研究发现，认知障碍组和认知正常组32个步态参数和14个眼动参数存在差异，眼动追踪、步态分析和双重任务评估的整合为认知障碍早期筛查提供了有力的数据支持[38]。研究建议整合更多生理信号，除了眼动和步态特征，还可以整合面部表情（如面部动作单位的激活情况）、声音特征（如音高、响度、语速）等多模态数据。此外，有学者构建了一种多模态数据融合的在线学习情感计算模型，通过采集面部表情、语音和文本数据，并利用情感识别模型提取各模态的情感识别结果，结果表明最优模型的平均识别精度较单模态情感识别提高了14.51%[39]。
5　展望
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5.1　面部表情数据集
面部识别是一项数据驱动的任务，训练一个足够深的神经网络以捕捉与面部表情相关的微小变形需要大量的训练数据[40]。理想的面部表情数据集包括多样化的图像样本，涵盖不同年龄、性别等，有利于提升跨年龄、跨性别和跨文化的识别能力。此外，由于缺乏统一的视频录制环境，视频质量存在较大差异，而视频清晰度与预测得分有关。未来的模型应考虑如何优化视频清晰度，随着面部表情识别研究逐渐关注复杂的实际环境，目前研究者们开始利用深度学习技术应对光照变化、遮挡和头部姿势不一致等问题，以提高识别准确性[18]。建议工作者在指导病人进行筛查时，尽量营造一个规范化的筛查环境，同时需要政府鼓励新产业的发展及在医院的推广与应用。
5.2　数据集偏倚和不均衡分布
由于面部表情数据集的采集条件和标注存在差异，导致不同数据集间也存在数据偏差和标注不一致的问题[29]。许多研究在特定的数据集上评估其算法，通常能取得较好的结果。然而，这些算法往往缺乏在其他未见数据上的普适性，而探索深度学习中的领域适应和知识迁移技术有望减少数据集之间的偏差。另一个常见的问题是面部表情类别的分布不平衡使得某些表情的识别变得更加困难，如笑脸较容易收集和标注，但像厌恶、恐惧等较少见的表情就比较难处理。由于这种不平衡现象，在评估模型使用平均精度（即对所有类别给予相同权重）时，导致模型表现明显下降[31]。建议在模型训练过程中给予不同类别的表情更合适的权重，从而提高性能。此外，专业人员可以通过与病人的互动，识别和记录这些较少见的情绪表现。
5.3　多模态情感识别
在实际应用中，病人情感的表达涉及多个方面，面部表情只是其中之一。近年来，基于深度学习的多模态情感分析方法引起了广泛关注，融合技术被用于结合不同数据来源的信息。决策级融合将不同模型的结果进行后期组合，而特征融合则在早期阶段提取并合并各模态特征，形成完整的情感表示[39]。此外，面部表情与其他生理信息（如红外图像、三维面部数据和生理信号）的结合展现出良好的人机交互潜力[24]。可以利用这些技术，为病人提供更个性化的情感和健康状态分析，从而提升护理质量和病人体验。这种技术与护理实践的结合，将帮助护理人员更全面地理解和回应病人的情绪和需求。
6　小结
收起
面部筛查技术通过情绪图片识别、动态视频分析、半结构化访谈视频以及混合多模态识别等多种方式，为老年认知障碍的早期筛查提供了新的途径。尽管面部筛查技术在老年认知障碍筛查中展现出巨大潜力，但仍面临诸多挑战，如面部表情数据集的多样性和质量、数据集偏倚和均衡性、多模态情感识别的融合技术等。未来的研究应进一步优化视频清晰度，减少数据集之间的偏差，提高模型在不同数据集上的普适性，并探索更多生理信号的整合，以推动面部筛查技术的进一步发展。此外，构建适合我国居民的研究方案和技术，关注文化背景对面部情绪感知的影响，也是未来研究的重要方向。

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2026年第40卷第4期
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doi: 10.12102/j.issn.1009-6493.2026.04.020
  • 接收时间：2025-01-13
  • 首发时间：2026-10-28
  • 出版时间：2026-02-25
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  • 收稿日期：2025-01-13
  • 修回日期：2026-01-27
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    1湖北中医药大学护理学院，湖北 430065
    2湖北中医药大学附属医院（湖北省中医院）
    3湖北时珍实验室

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