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检索条件"机构=Chongqing Key Laboratory of Non-linear Circuit and Intelligent Information Processing"

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Offset or Onset Frame: A Multi-Stream Convolutional Neural Network with CapsuleNet Module for Micro-expression Recognition

Offset or Onset Frame: A Multi-Stream Convolutional Neural N...

引用

International Conference on intelligent Informatics and Biomedical Sciences (ICIIBMS)

作者： Nian Liu Xinyu Liu Zhihao Zhang Xueming Xu Tong Chen chool of Electronic and Information Engineering Southwest University Chongqing Key Laboratory of Non-linear Circuit and Intelligent Information Processing Chongqing China

ISBN: (数字)9781728180229

ISBN: (纸本)9781728180236

Micro-expression is a spontaneous facial expression, which may reveal people's real emotions. The micro-expression recognition has recently attracted much attention in psychology and computer vision community. In this paper, we designed a multi-stream Convolutional Neural Network (CNN) combined with the Capsule Network(CapsNet) *** CNNCapsNet, to improve the performance of micro-expression recognition. Firstly, both vertical and horizontal optical flow are computed from the onset to the apex, and from the apex to the offset frame respectively, which is the first time that the offset frame information has been taken into account in the field of micro-expression recognition. Secondly, these four optical flow images and the grayscale image of apex frame are input into the five-stream CNN model to extract features. Finally, CapsNet completes micro-expression recognition by learning the features extracted by CNN. The method proposed in this paper are evaluated using the Leave-One-Subject-Out (LOSO) cross-validation protocol on CASME II. The results show that the offset information, which is often neglected, is more important than onset information for the recognition task. Our CNNCapsNet framework can achieve the accuracy of 64.63% for the five-class micro-expression classification.

关键词： Computer vision Protocols Psychology Feature extraction Convolutional neural networks Task analysis Optical flow

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TSW-FD: A novel temporal and spatial domain weight analysis of feature difference for micro-expression spotting

TSW-FD: A novel temporal and spatial domain weight analysis ...

引用

2020 International Symposium on Automation, information and Computing, ISAIC 2020

作者： Zhang, Zhihao Mo, Fan Zhao, Ke Chen, Tong Fu, Xiaolan State Key Laboratory of Brain and Cognitive Science Institute of Psychology Chinese Academy of Sciences Beijing100101 China Department of Psychology University of Chinese Academy of Sciences Beijing100049 China Chongqing Key Laboratory of Non-linear Circuit and Intelligent Information Processing Southwest University Chongqing400715 China Chongqing Key Laboratory of Artificial Intelligence and Service Robot Control Technology Chongqing400715 China

The micro-expression spotting has recently attracted increasing attention from psychology and computer vision community, since embraced in the second facial Micro-Expression Grand Challenge (MEGC 2019). Different from the original feature difference (FD) analysis, in this paper, we proposed a novel temporal and spatial domain weight analysis of feature difference (TSW-FD) to achieve micro-expression spotting. The experimental results showed that TSW-FD improved 17.86% and 24.21% in F1-Score comparing to the FD in CASME Ⅱ and SMIC-E-HS. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Finite difference method

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A Methodology for Palm Vein Image Enhancement and Visualization

A Methodology for Palm Vein Image Enhancement and Visualizat...

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2016 IEEE International Conference of Online Analysis and Computing Science(ICOACS 2016)

作者： Min Peng Chongyang Wang Tong Chen Guangyuan Liu School of Electronic and Information Engineering Southwest University Chongqing Key Laboratory of Non-linear Circuit and Intelligent Information Processing Southwest University

Palm vein recognition is a developing biometric identification *** can be used in physical security and information security for selective control of access to a place or *** to the palm vein recognition,vein enhancement is generally required for a better *** this paper,we propose a vein enhancement method combining Karhunen-Loeve(KL) transform and hue-preserving color image enhancement *** results show that the proposed method is robust to shadow affect,can give better visualization results,and can significantly improve the contrast and contour details of image(the contrast ratio increases by 2.3%,edge intensity increases by 8.8%,and definition increases by 66.9%) comparing to the traditional enhancement methods.

关键词： palm vein recognition palm vein enhancement multispectral image KL transform color enhancement formatting

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Face recognition from near-infrared images with convolutional neural network

Face recognition from near-infrared images with convolutiona...

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International Conference on Wireless Communications and Signal processing (WCSP)

作者： Xiaotong Zhang Min Peng Tong Chen Chonqging Key Laboratory of Non-linear Circuit and Intelligent Information Processing Southwest University China

ISBN: (纸本)9781509028610

Owing to the vigorous development of face recognition, near-infrared (NIR) face recognition technology with light insensitivity has attracted increasing attention. However, the traditional methods for NIR face recognition feature the hand-crafted feature design. In this paper, we present a convolutional neural network (CNN) for NIR face recognition. CNN is a multiplayer feed-forward neural network which can automatically learn the features from the raw images and provide partial invariance to illumination, scale and deformation. Experimental results on PolyU-NIRFD database show that our proposed CNN architecture has higher recognition rate compared with the traditional recognition methods, such as Gabor-directional binary code (GDBC), Zernike moments and Hermite kernels (ZMHK).

关键词： Face recognition Feature extraction Neural networks Lighting Face Convolutional codes Databases

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Transfer Learning Technique with VGG-16 for Near-Infrared Facial Expression Recognition 2

Transfer Learning Technique with VGG-16 for Near-Infrared Fa...

引用

2021 2nd International Workshop on Electronic communication and Artificial Intelligence, IWECAI 2021

作者： Atabansi, Chukwuemeka C. Chen, Tong Cao, Ranlei Xu, Xueming School of Electronic and Information Engineering Southwest University Beibei District Chongqing400715 China Chongqing Key Laboratory of Nonlinear Circuit and Intelligent Information Processing Southwest University Beibei District Chongqing400715 China

In this paper, we investigate a deep learning vgg-16 network architecture for facial expression recognition under active near-infrared illumination condition and background. In particular, we consider the concept of transfer learning whereby features learned from high resolution images of huge datasets can be used to train a model of relatively small dataset without loosing the generalization ability. The pre-trained vgg-16 network architecture with transfer learning technique has been trained and validated on the Oulu-CASIA NIR dataset comprising of six (6) distinct facial expressions, and average test accuracy of 98.11% was achieved. The validation on our test data using the confusion, the precision, and the recall matrix reveals that our method achieves better results in comparison with the other methods in the literature. © Published under licence by IOP Publishing Ltd.

关键词： Network architecture

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A methodology for palm vein image enhancement and visualization

A methodology for palm vein image enhancement and visualizat...

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IEEE International Conference of Online Analysis and Computing Science (ICOACS)

作者： Min Peng Chongyang Wang Tong Chen Guangyuan Liu Chongqing Key Laboratory of Non-linear Circuit and Intelligent Information Processing Southwest University Chongqing China School of Electronic and Information Engineering Southwest University Chongqing China

ISBN: (纸本)9781467377560

Palm vein recognition is a developing biometric identification technology. It can be used in physical security and information security for selective control of access to a place or resource. Prior to the palm vein recognition, vein enhancement is generally required for a better recognition. In this paper, we propose a vein enhancement method combining Karhunen-Loeve (KL) transform and hue-preserving color image enhancement technology. Experimental results show that the proposed method is robust to shadow affect, can give better visualization results, and can significantly improve the contrast and contour details of image (the contrast ratio increases by 2.3%, edge intensity increases by 8.8%, and definition increases by 66.9%) comparing to the traditional enhancement methods.

关键词： Veins Transforms Decision support systems Image recognition Image color analysis Instruments Visualization

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An Edge-based Distributed Algorithm for Economic Dispatch in Power Systems

An Edge-based Distributed Algorithm for Economic Dispatch in...

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International Conference on Control and Automation (ICCA)

作者： Lifeng Zheng Zhenyuan Du Huaqing Li Chongqing Key Laboratory of Non-linear Circuits and Intelligent Information Processing Southwest University Chongqing PR China

ISBN: (数字)9781728190938

ISBN: (纸本)9781728190945

This paper proposes an edge-based distributed primal-dual algorithm for economic dispatch problem (EDP), in which a collection of networked generators cooperatively minimizes the sum of all local cost functions while satisfying some coupling constraints. Different from the existing results, this paper applies the techniques, such as the factorization of weighted Laplacian and the spectral decomposition, to prove the convergence of the proposed algorithm. It proves that the proposed distributed algorithm converges provided that the step-size is positive and lower than an exact upper bound. Strong convexity of each local cost function and Lipschitz continuity of its gradient are employed to ensure a linear convergence rate of the proposed algorithm. Finally, two numerical simulations are presented to show the performance of the proposed algorithm.

关键词： Generators Convergence linear matrix inequalities Distributed algorithms Laplace equations Cost function Economics

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A Fusion Shallow and Deep Features Network for Facial StO2 Stress Classification

A Fusion Shallow and Deep Features Network for Facial StO2 S...

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IEEE information Technology, Networking, Electronic and Automation Control Conference

作者： Haolin Xia Sirui Sun Xinyu Liu Ju Zhou Hanpu Wang Tong Chen School of Electronic and Information Engineering Southwest University Chongqing China Key Laboratory of Non-Linear Circuit and Intelligent Information Processing of Chongqing Chongqing China Institute of Legal Psychology and Intelligent Computing Chongqing China

Stress is an emotional state that affects our lives, so it is vital to detect it accurately. Numerous studies have shown that tissue oxygen saturation (StO2) is an effective physiological signal for identifying different stress states. However, previous StO2 feature extraction methods relied too heavily on baselines to be realized in practice. In this paper, a two-stream network combining shallow and deep features, called CRNet, is proposed for baseline-independent StO2 stress classification. In CRNet, shallow features are extracted using a CNN network and deep features are extracted using a ResNet network. For the extracted high-dimensional deep features, MLP module is employed for dimensionality reduction, which can effectively combine the two levels of features and improve the feature extraction capability of the network. Experimental results show that CRNet can significantly improve the performance of StO2 stress classification to the state-of-the-art (SOTA) even when using only raw imbalanced data.

关键词： Dimensionality reduction Automation Anxiety disorders Feature extraction Physiology Stress

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Facial StO2: A New Promising Biometric Identity

Facial StO2: A New Promising Biometric Identity

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Dairong Peng Sirui Sun Xinyu Liu Ju Zhou Tong Chen Institute of Legal Psychology and Intelligent Computing Southwest University Chongqing China Chongqing Key Laboratory of Non-linear Circuit and Intelligent Information Processing Chongqing China School of Electronic and Information Engineering Southwest University Chongqing China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

In this paper, we introduce a new biometric identity, facial tissue oxygen saturation (StO2). StO2 is an index of blood oxygen content in tissues and is related to blood vessel distribution pattern and metabolic rate. Experimental results show that classification accuracy can reach 83.33% in 42 participants with different stress states by using StO2 as the only input to the ResNet-50 model. We also proposed a module called StO2Net to eliminate the effects of stress on classification. The highest accuracy can reach up to 90.48% when the module is used. This pilot study shows that facial StO2 can be a promising biometric feature for identity recognition.

关键词： Databases Face recognition Biological system modeling Aging Security Bioinformatics Stress

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Long Video Micro-expression Spotting Based On OCC Theory

Long Video Micro-expression Spotting Based On OCC Theory

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： BingTong Liu ZheYuan Zhang Ju Zhou HanPu Wang Tong Chen Institute of Legal Psychology and Intelligent Computing Southwest University Chongqing China Chongqing Key Laboratory of Non-Linear Circuit and Intelligent Information Processing Chongqing China School of Electronic and Information Engineering Southwest University Chongqing China

The generation of micro-expression (ME) is unconscious and unaffected by human subjective consciousness. It can reveal hidden emotions in humans, which has important applications in the domains of psychological diagnosis, lie detection, and so on. ME detection, which aims to locate ME apex frames in long videos, is the foundation of ME recognition. In this paper, we propose a ME detection method based on One-Class Classification (OCC) theory for long videos. The method is the first to use neutral expression frames to detect MEs, which addresses the problem of insufficient training samples in ME detection. We design a geometric motion feature based on the prior knowledge from the FACS and utilize it in conjunction with the features extracted by CNN to train separate one-class classifiers for ME detection. Finally, we evaluate the proposed method on publicly available datasets CASME I, CASME II, SAMM, and a mixed database comprising data from the three datasets, to verify the feasibility of the proposed method in both single-database and cross-database ME detection. The experimental results show that our method outperforms three other ME detection methods, demonstrating its ability to more accurately locate ME apex frames in long videos.

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