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作者： Li, Shuang Dong, Xiaoli Shi, Yuan Lu, Baoli Sun, Linjun Li, Wenfa Institute of Semiconductors Chinese Academy of Sciences Beijing China Cognitive Computing Technology Joint Laboratory Wave Group Beijing China Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing China Shenzhen Wave Kingdom Co. Ltd. Shenzhen China College of Robotics Beijing Union University Beijing China

Head pose classification is an important part of the preprocessing process of face recognition, which can independently solve application problems related to multi-angle. But, due to the impact of the COVID-19 coronavirus pandemic, more and more people wear masks to protect themselves, which covering most areas of the face. This greatly affects the performance of head pose classification. Therefore, this article proposes a method to classify the head pose with wearing a mask. This method focuses on the information that is helpful for head pose classification. First, the H-channel image of the HSV color space is extracted through the conversion of the color space. Then use the line portrait to extract the contour lines of the face, and train the convolutional neural networks to extract features in combination with the grayscale image. Finally, stacked generalization technology is used to fuse the output of the three classifiers to obtain the final classification result. The results on the MAFA dataset show that compared with the current advanced algorithm, the accuracy of our method is 94.14% on the front, 86.58% on the more side, and 90.93% on the side, which has better performance. © 2021 John Wiley & Sons, Ltd.

关键词： Face recognition

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Continuous learning of face attribute synthesis

arXiv

引用

arXiv 2020年

作者： Ning, Xin Li, Weijun Dong, Xiaoli Xu, Shaohui Nan, Fangzhe Yao, Yuanzhou Institute of Semiconductors Chinese Academy of Sciences Cognitive Computing Technology Joint Laboratory Wave Group Beijing China Cognitive Computing Technology Joint Laboratory Wave Group Beijing China College of Information Engineering Sichuan Agricultural University Ya'an Sichuan China

The generative adversarial network (GAN) exhibits great superiority in the face attribute synthesis task. However, existing methods have very limited effects on the expansion of new attributes. To overcome the limitations of a single network in new attribute synthesis, a continuous learning method for face attribute synthesis is proposed in this work. First, the feature vector of the input image is extracted and attribute direction regression is performed in the feature space to obtain the axes of different attributes. The feature vector is then linearly guided along the axis so that images with target attributes can be synthesized by the decoder. Finally, to make the network capable of continuous learning, the orthogonal direction modification module is used to extend the newly-added attributes. Experimental results show that the proposed method can endow a single network with the ability to learn attributes continuously, and, as compared to those produced by the current state-of-the-art methods, the synthetic attributes have higher accuracy. Copyright © 2020, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Continuous Learning of Face Attribute Synthesis

Continuous Learning of Face Attribute Synthesis

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International Conference on Pattern Recognition

作者： Xin Ning Weijun Li Xiaoli Dong Shaohui Xu Fangzhe Nan Yuanzhou Yao Institute of Semiconductors Chinese Academy of Sciences Cognitive Computing Technology Joint Laboratory Wave Group Beijing China Cognitive Computing Technology Joint Laboratory Wave Group Beijing China College of Information Engineering Sichuan Agricultural University Ya'an Sichuan China

关键词： Learning systems Face recognition Process control Interference Feature extraction Generative adversarial networks Space exploration

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AGCNN: Adaptive Gabor Convolutional Neural Networks with Receptive Fields for Vein Biometric Recognition

AGCNN: Adaptive Gabor Convolutional Neural Networks with Rec...

引用

作者： Zhang, Yakun Li, Weijun Zhang, Liping Ning, Xin Sun, Linjun Lu, Yaxuan Laboratory of Artificial Neural Networks and High-Speed Circuits Institute of Semiconductors Chinese Academy of Sciences Beijing China Center of Materials Science and Optoelectronics Engineering & School of Microelectronics University of Chinese Academy of Sciences Beijing China Cognitive Computing Technology Joint Laboratory Wave Group Beijing China

In recent years, finger vein recognition has attracted more attention and research as a secure method of identification. Convolutional neural networks have achieved great success in the field of finger vein recognition, yet they suffer from high computational complexity, large parameters, and other challenges. To solve these problems, we propose a Gabor convolutional neural network with receptive fields. We use Gabor filters with receptive field properties to design Gabor convolutional layers. Then we replace the conventional convolutional layer with the Gabor convolutional layer;analyze the influence of different loss functions, convolution kernel size, and feature size on the network model;and choose the most suitable model parameters and loss function. Finally, we systematically investigate comparative performance using AGCNN and CNNs in different finger vein databases. Experimental results show that the parameter complexity of AGCNN is significantly less than that of CNNs with a slight performance decrease. © 2020 John Wiley & Sons, Ltd.

关键词： Gabor filters

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Towards Efficient and Diverse Generative Model for Unconditional Human Motion Synthesis 24

Towards Efficient and Diverse Generative Model for Unconditi...

引用

32nd ACM International Conference on Multimedia, MM 2024

作者： Yu, Hua Liu, Weiming Bai, Jiapeng Gui, Xu Hou, Yaqing Ong, Yewsoon Zhang, Qiang Key Laboratory of Social Computing and Cognitive Intelligence Ministry of Education School of Computer Science and Technology Dalian University of Technology Dalian China College of Computer Science Zhejiang University Hangzhou China National and Local Joint Engineering Laboratory of Computer Aided Design School of Software Engineering Dalian University Dalian China College of Computing and Data Science Nanyang Technological University Singapore Singapore Yonsei University Seoul Korea Republic of

ISBN: (纸本)9798400706868

Recent generative methods have revolutionized the way of human motion synthesis, such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Denoising Diffusion Probabilistic Models (DMs). These methods have gained significant attention in human motion fields. However, there are still challenges in unconditionally generating highly diverse human motions from a given distribution. To enhance the diversity of synthesized human motions, previous methods usually employ deep neural networks (DNNs) to train a transport map that transforms Gaussian noise distribution into real human motion distribution. According to Figalli's regularity theory, the optimal transport map computed by DNNs frequently exhibits discontinuities. This is due to the inherent limitation of DNNs in representing only continuous maps. Consequently, the generated human motions tend to heavily concentrate on densely populated regions of the data distribution, resulting in mode collapse or mode mixture. To address the issues, we propose an efficient method called MOOT for unconditional human motion synthesis. First, we utilize a reconstruction network based on GRU and transformer to map human motions to latent space. Next, we employ convex optimization to match the noise distribution with the latent space distribution of human motions through the Optimal Transport (OT) map. Then, we combine the extended OT map with the generator of reconstruction network to generate new human motions. Thereby overcoming the issues of mode collapse and mode mixture. MOOT generates a latent code distribution that is well-behaved and highly structured, providing a strong motion prior for various applications in the field of human motion. Through qualitative and quantitative experiments, MOOT achieves state-of-the-art results surpassing the latest methods, validating its superiority in unconditional human motion generation. © 2024 ACM.

关键词： Generative adversarial networks

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Multi-angle Head Pose Classification when Wearing the Mask for Face Recognition under the COVID-19 Coronavirus Epidemic

Multi-angle Head Pose Classification when Wearing the Mask f...

引用

High Performance Big Data and Intelligent Systems (HPBD&IS)

作者： Shuang Li Xin Ning Lina Yu Liping Zhang Xiaoli Dong Yuan Shi Wei He Cognitive Computing Technology Joint Laboratory Wave Group Beijing China School of Microelectronics University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728165127

ISBN: (纸本)9781728158976

Head pose classification is widely used for the preprocessing before face recognition and multi-angle problems, because algorithms such as face recognition often require the input image to be a front face. But affected by the COVID-19 pandemic, people wear face masks to protect themselves safe, which makes cover most areas of the face. This makes some common algorithms cannot be applied to head pose classification in the new situation. Therefore, this paper established a method HGL to deal with the head pose classification by adopting color texture analysis of images and line portrait. The proposed HGL method combines the H-channel of the HSV color space with the face portrait and grayscale image, and train the CNN to extract features for classification. The evaluation on MAFA dataset shows that compared with the algorithms based on facial landmark detection and convolutional neural network, the proposed method has achieved a better performance (Front accuracy: 93.64%, Side accuracy: 87.17 % ).

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Information sieve: Content leakage reduction in end-to-end prosody transfer for expressive speech synthesis

arXiv

引用

arXiv 2021年

作者： Dai, Xudong Gong, Cheng Wang, Longbiao Zhang, Kaili Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China Tianjin University-Huiyan Technology Joint AI Lab Tianjin University Tianjin China

Expressive neural text-to-speech (TTS) systems incorporate a style encoder to learn a latent embedding as the style information. However, this embedding process may encode redundant textual information. This phenomenon is called content leakage. Researchers have attempted to resolve this problem by adding an ASR or other auxiliary supervision loss functions. In this study, we propose an unsupervised method called the "information sieve" to reduce the effect of content leakage in prosody transfer. The rationale of this approach is that the style encoder can be forced to focus on style information rather than on textual information contained in the reference speech by a well-designed downsample-upsample filter, i.e., the extracted style embeddings can be downsampled at a certain interval and then upsampled by duplication. Furthermore, we used instance normalization in convolution layers to help the system learn a better latent style space. Objective metrics such as the significantly lower word error rate (WER) demonstrate the effectiveness of this model in mitigating content leakage . Listening tests indicate that the model retains its prosody transferability compared with the baseline models such as the original GST-Tacotron and ASR-guided Tacotron. Copyright © 2021, The Authors. All rights reserved.

关键词： Sieves

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A local descriptor with physiological characteristic for finger vein recognition

arXiv

引用

arXiv 2020年

作者： Zhang, Liping Li, Weijun Ning, Xin Institute of Semiconductors Chinese Academy of Sciences Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology School of Microelectronics University of Chinese Academy of Sciences Cognitive Computing Technology Joint Laboratory Wave Group Beijing China

Local feature descriptors exhibit great superiority in finger vein recognition due to their stability and robustness against local changes in images. However, most of these are methods use general-purpose descriptors that do not consider finger vein-specific features. In this work, we propose a finger vein-specific local feature descriptors based physiological characteristic of finger vein patterns, i.e., histogram of oriented physiological Gabor responses (HOPGR), for finger vein recognition. First, prior of directional characteristic of finger vein patterns is obtained in an unsupervised manner. Then the physiological Gabor filter banks are set up based on the prior information to extracte the physiological responses and orientation. Finally, to make feature has robustness against local changes in images, histogram is generated as output by dividing the image into non-overlapping cells and overlapping blocks. Extensive experimental results on several databases clearly demonstrate that the proposed method outperforms most current state-of-the-art finger vein recognition methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Gabor filters

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Adversarial U-Network for Predicting Blood Oxygen Level-Dependent Time Series

Adversarial U-Network for Predicting Blood Oxygen Level-Depe...

引用

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Bao, Cong Zheng, Weihao Zhang, Qin Yang, Songyu Yao, Zhijun Hu, Bin Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou China School of Medical Technology Beijing Institute of Technology Beijing China Cas Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Shanghai China Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University Institute of Semiconductors Chinese Academy of Sciences Lanzhou China

ISBN: (纸本)9798350337488

Functional magnetic resonance imaging (fMRI) plays a vital role in brain science as it measures and maps brain activity through the analysis of blood flow changes, offering valuable insights into cognitive functions and neural processes. However, due to the intricacy and dynamism of brain activity, conventional approaches failed to accurately predict the blood oxygen level-dependent (BOLD) time series in fMRI data. To tackle this issue, we proposed an end-to-end adversarial U-network (AUN) to verify the predictability of existing BOLD signals in primary cortex (i.e., primary visual, primary motor and primary sensory) and higher cortex(i.e., dorsolateral prefrontal and posterior cingulate). The model combined the U-network architecture and adversarial strategy to ensure that the predicted results capture both the intricate nonlinear details and the overall distribution characteristics. We performed the experiment using the Human Connectome Project (HCP) database. The results demonstrated the predictability of both primary and higher cortex, with primary cortex showing higher predictability. Additionally, the AUN performed better than other popular methods. We also found the improvement in dynamic functional connectivity (dFC) metrics through accurate prediction. The above results confirm the feasibility of predicting BOLD signals and their potential application in clinical settings.1 © 2023 IEEE.

关键词： adversarial paradigm blood oxygen level-dependent series prediction dynamic functional connectivity functional magnetic resonance imaging

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A Portable System of Mental Fatigue Detection and Mitigation based on Physiological Signals

A Portable System of Mental Fatigue Detection and Mitigation...

引用

2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Wu, Chao Tian, Fuze Shi, Qiuxia Zhao, Qinglin Hu, Bin Lanzhou University Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Lanzhou China Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University Institute of Semiconductors Chinese Academy of Sciences Lanzhou China Ministry of Education Lanzhou China

ISBN: (纸本)9781665468190

Mental fatigue of the brain will cause the weakening of the psychological and physiological functions of the human body, which increases the risk of mistakes and accidents. This paper designs a mental fatigue detection and mitigation system based on human physiological signals and classical music. Through the fatigue assessment and mitigation experiment on 20 healthy subjects, it is verified that the system is able to judge the mental fatigue state of the subjects and play classical music to mitigate fatigue via acquiring and analyzing (alpha+theta)/beta of electroencephalogram (EEG) and heart rate variability (HRV) of ballistocardiogram (BCG). The designed system realizes signal acquisition and fatigue analysis only through using portable device, this makes it have certain application value in office building, hospital, aircraft driving and other scenes. © 2022 IEEE.

关键词： Electroencephalography

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