Understanding of facial features in face perception: insights from deep convolutional neural networks

IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Frontiers in Computational Neuroscience Pub Date : 2024-04-09 DOI:10.3389/fncom.2024.1209082
Qianqian Zhang, Yueyi Zhang, Ning Liu, Xiaoyan Sun
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Abstract

IntroductionFace recognition has been a longstanding subject of interest in the fields of cognitive neuroscience and computer vision research. One key focus has been to understand the relative importance of different facial features in identifying individuals. Previous studies in humans have demonstrated the crucial role of eyebrows in face recognition, potentially even surpassing the importance of the eyes. However, eyebrows are not only vital for face recognition but also play a significant role in recognizing facial expressions and intentions, which might occur simultaneously and influence the face recognition process.MethodsTo address these challenges, our current study aimed to leverage the power of deep convolutional neural networks (DCNNs), an artificial face recognition system, which can be specifically tailored for face recognition tasks. In this study, we investigated the relative importance of various facial features in face recognition by selectively blocking feature information from the input to the DCNN. Additionally, we conducted experiments in which we systematically blurred the information related to eyebrows to varying degrees.ResultsOur findings aligned with previous human research, revealing that eyebrows are the most critical feature for face recognition, followed by eyes, mouth, and nose, in that order. The results demonstrated that the presence of eyebrows was more crucial than their specific high-frequency details, such as edges and textures, compared to other facial features, where the details also played a significant role. Furthermore, our results revealed that, unlike other facial features, the activation map indicated that the significance of eyebrows areas could not be readily adjusted to compensate for the absence of eyebrow information. This finding explains why masking eyebrows led to more significant deficits in face recognition performance. Additionally, we observed a synergistic relationship among facial features, providing evidence for holistic processing of faces within the DCNN.DiscussionOverall, our study sheds light on the underlying mechanisms of face recognition and underscores the potential of using DCNNs as valuable tools for further exploration in this field.
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理解人脸感知中的面部特征:深度卷积神经网络的启示
导言:人脸识别是认知神经科学和计算机视觉研究领域长期关注的课题。其中一个重点是了解不同面部特征在识别个人时的相对重要性。以前对人类的研究表明,眉毛在人脸识别中起着至关重要的作用,其重要性甚至可能超过眼睛。然而,眉毛不仅对人脸识别至关重要,而且在识别面部表情和意图方面也起着重要作用,而面部表情和意图可能同时出现并影响人脸识别过程。在这项研究中,我们通过选择性地屏蔽 DCNN 输入中的特征信息,研究了各种面部特征在人脸识别中的相对重要性。结果我们的研究结果与之前的人类研究结果一致,眉毛是人脸识别中最关键的特征,其次依次是眼睛、嘴巴和鼻子。结果表明,与其他面部特征相比,眉毛的存在比其特定的高频细节(如边缘和纹理)更为关键,而在其他面部特征中,细节也发挥着重要作用。此外,我们的结果还显示,与其他面部特征不同,激活图显示眉毛区域的重要性不能轻易调整以弥补眉毛信息的缺失。这一发现解释了为什么遮蔽眉毛会导致更严重的人脸识别能力缺陷。总之,我们的研究揭示了人脸识别的内在机制,并强调了使用 DCNN 作为该领域进一步探索的宝贵工具的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Frontiers in Computational Neuroscience
Frontiers in Computational Neuroscience MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
5.30
自引率
3.10%
发文量
166
审稿时长
6-12 weeks
期刊介绍: Frontiers in Computational Neuroscience is a first-tier electronic journal devoted to promoting theoretical modeling of brain function and fostering interdisciplinary interactions between theoretical and experimental neuroscience. Progress in understanding the amazing capabilities of the brain is still limited, and we believe that it will only come with deep theoretical thinking and mutually stimulating cooperation between different disciplines and approaches. We therefore invite original contributions on a wide range of topics that present the fruits of such cooperation, or provide stimuli for future alliances. We aim to provide an interactive forum for cutting-edge theoretical studies of the nervous system, and for promulgating the best theoretical research to the broader neuroscience community. Models of all styles and at all levels are welcome, from biophysically motivated realistic simulations of neurons and synapses to high-level abstract models of inference and decision making. While the journal is primarily focused on theoretically based and driven research, we welcome experimental studies that validate and test theoretical conclusions. Also: comp neuro
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