摘要
面孔识别是人类社会交往中的核心认知能力。近年来,深度卷积神经网络(deep convolutional neural network,DCNN)在模拟和理解面孔加工中展现出强大的能力,为探究人类面孔识别的行为表现和神经机制提供了新的视角。因此,围绕识别能力、行为效应与神经机制三个方面,本文系统综述了DCNN与人类在面孔识别中的异同:(1)首先,DCNN是否具备与人类相当的面孔识别能力?从面孔身份、性别、情绪等特征方面出发,评估DCNN在面孔识别任务中的表现;(2)其次,尽管DCNN在识别准确性上表现优异,其加工策略是否与人类的行为机制一致?基于经典的面孔加工效应(如倒置效应、种族效应、熟悉性效应等)分析DCNN与人类加工策略上的相似性与差异性;(3)进一步,DCNN的内部表征是否与人类面孔加工的神经机制相类似?从结构层级性和功能专门化两个方面,比较其表征方式与人类面孔识别系统的神经基础之间的对应关系。当前模型在鲁棒性与泛化性、结果解释力、生物视觉系统模拟等方面仍存在一定局限性,未来研究也可进一步探索其与多模态网络及生成对抗网络的融合潜力。
features,which are then passed through one or more fully connected layers to perform classification tasks.Consequently,there has been a growing interest in investigating the applications of DCNNs in face recognition.First,this review examines the performance of DCNNs in identifying key facial attributes.Although most DCNNs are trained only for face identity tasks,they can still infer social information such as gender and expression.In addition,this review also discusses the similarities and differences between DCNNs and humans in well-known face processing phenomena,such as the inversion,own-race,and familiarity effects.Evidence suggests that DCNNs can produce face-specific cognitive effects similar to those observed in humans.To better understand the computational validity of DCNNs,this review compares their internal representations with the neural mechanisms involved in human face recognition.On the one hand,this paper analyzes the hierarchical processing architecture that emerges in trained DCNNs and evaluates its correspondence with the hierarchical structure of the human visual system,spanning from early visual areas(e.g.,V1–V4)to higher-level face-selective regions such as the FFA.On the other hand,this review further discusses evidence for brain-like functional specialization within DCNNs,examining whether units selective to different facial attributes can be mapped onto the functionally specialized cortical areas observed in neuroimaging and electrophysiological studies.Lastly,this paper highlights several limitations of current models and outlines promising directions for future research.First,although DCNNs excel at face recognition,they remain far less robust than humans when faced with challenges such as viewpoint shifts,image distortions,adversarial perturbations,and limited training data.Second,although DCNNs exhibit behavioral effects like those observed in humans,there are multiple possible explanations for the underlying mechanisms responsible for these phenomena.The DCNN models examined in different studies often vary in terms of architecture,task objectives,and training datasets,which may affect the comparability of their results.Third,the extent to which current models can capture essential features of the biological visual system remains unclear.Specifically,many DCNNs operate as feedforward architectures and lack critical elements such as recurrent processing,top-down feedback,and dynamic attentional modulation,all of which are fundamental characteristics of the human visual system.Fourth,current neural network models primarily focus on the perceptual stage underlying face recognition.Future research should aim to incorporate semantic-level processing to more fully capture the complexity of human face perception.Fifth,generative Adversarial Networks(GANs)have recently attracted significant attention,which are powerful tools for generating diverse facial stimuli,enabling more controlled and flexible investigations of face perception.Integrating GANs with DCNNs has also enhanced our understanding of the mechanisms underlying facial representation,making it a promising direction for future research.
作者
程羽慧
申天宇
路子童
袁祥勇
蒋毅
Cheng Yuhui;Shen Tianyu;Lu Zitong;Yuan Xiangyong;Jiang Yi(School of Psychology,Nanjing Normal University,Nanjing,210097;Massachusetts Institute of Technology McGovern Institute for Brain Research,Cambridge,MA,02139;State Key Laboratory of Cognitive Science and Mental Health,Institute of Psychology,Chinese Academy of Sciences,16 Lincui Road,Beijing,100101;Department of Psychology,University of Chinese Academy of Sciences,19A Yuquan Road,Beijing,100049)
出处
《心理科学》
北大核心
2025年第4期814-825,共12页
Journal of Psychological Science
基金
国家自然科学基金青年项目(32400864)
南京师范大学引进人才科研启动项目(184080H201A45)
国家社会科学基金青年项目(23CYY048)的资助。
关键词
面孔识别
卷积神经网络
梭状回面孔区
层级结构
功能分化
face recognition
convolutional neural network
fusiform face area
hierarchical structure
functional specialization
