Machine to brain: facial expression recognition using brain machine generative adversarial networks.

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
ACS Applied Electronic Materials Pub Date : 2024-06-01 Epub Date: 2023-02-22 DOI:10.1007/s11571-023-09946-y
Dongjun Liu, Jin Cui, Zeyu Pan, Hangkui Zhang, Jianting Cao, Wanzeng Kong
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引用次数: 0

Abstract

The human brain can effectively perform Facial Expression Recognition (FER) with a few samples by utilizing its cognitive ability. However, unlike the human brain, even the well-trained deep neural network is data-dependent and lacks cognitive ability. To tackle this challenge, this paper proposes a novel framework, Brain Machine Generative Adversarial Networks (BM-GAN), which utilizes the concept of brain's cognitive ability to guide a Convolutional Neural Network to generate LIKE-electroencephalograph (EEG) features. More specifically, we firstly obtain EEG signals triggered from facial emotion images, then we adopt BM-GAN to carry out the mutual generation of image visual features and EEG cognitive features. BM-GAN intends to use the cognitive knowledge learnt from EEG signals to instruct the model to perceive LIKE-EEG features. Thereby, BM-GAN has a superior performance for FER like the human brain. The proposed model consists of VisualNet, EEGNet, and BM-GAN. More specifically, VisualNet can obtain image visual features from facial emotion images and EEGNet can obtain EEG cognitive features from EEG signals. Subsequently, the BM-GAN completes the mutual generation of image visual features and EEG cognitive features. Finally, the predicted LIKE-EEG features of test images are used for FER. After learning, without the participation of the EEG signals, an average classification accuracy of 96.6 % is obtained on Chinese Facial Affective Picture System dataset using LIKE-EEG features for FER. Experiments demonstrate that the proposed method can produce an excellent performance for FER.

机器到大脑:使用大脑机器生成对抗网络的面部表情识别
人脑可以利用其认知能力,通过少量样本有效地进行面部表情识别(FER)。然而,与人脑不同的是,即使是训练有素的深度神经网络也会受到数据的影响,缺乏认知能力。为了应对这一挑战,本文提出了一个新颖的框架--脑机生成对抗网络(Brain Machine Generative Adversarial Networks,BM-GAN),它利用大脑认知能力的概念来引导卷积神经网络生成 LIKE 脑电图(EEG)特征。具体来说,我们首先获取面部情绪图像触发的脑电信号,然后采用 BM-GAN 实现图像视觉特征和脑电图认知特征的相互生成。BM-GAN 利用从 EEG 信号中学到的认知知识来指导模型感知 LIKE-EEG 特征。因此,BM-GAN 在 FER 方面具有类似人脑的卓越性能。建议的模型由 VisualNet、EEGNet 和 BM-GAN 组成。更具体地说,VisualNet 可以从面部情绪图像中获取图像视觉特征,EEGNet 可以从 EEG 信号中获取 EEG 认知特征。随后,BM-GAN 完成图像视觉特征和 EEG 认知特征的相互生成。最后,预测出的测试图像 LIKE-EEG 特征将用于 FER。经过学习,在没有脑电信号参与的情况下,使用 LIKE-EEG 特征进行 FER,在中国人脸情感图像系统数据集上获得了 96.6 % 的平均分类准确率。实验证明,所提出的方法能为 FER 带来卓越的性能。
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来源期刊
CiteScore
7.20
自引率
4.30%
发文量
567
期刊介绍: ACS Applied Electronic Materials is an interdisciplinary journal publishing original research covering all aspects of electronic materials. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials science, engineering, optics, physics, and chemistry into important applications of electronic materials. Sample research topics that span the journal's scope are inorganic, organic, ionic and polymeric materials with properties that include conducting, semiconducting, superconducting, insulating, dielectric, magnetic, optoelectronic, piezoelectric, ferroelectric and thermoelectric. Indexed/​Abstracted: Web of Science SCIE Scopus CAS INSPEC Portico
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