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A partition approach for robust gait recognition based on gait template fusion Research Articles

Kejun Wang, Liangliang Liu, Xinnan Ding, Kaiqiang Yu, Gang Hu,heukejun@126.com,liuliangliang@hrbeu.edu.cn,dingxinnan@hrbeu.edu.cn,yukaiqiang@hrbeu.edu.cn,hugang@hrbeu.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 5,   Pages 615-766 doi: 10.1631/FITEE.2000377

Abstract: has significant potential for remote human identification, but it is easily influenced by identity-unrelated factors such as clothing, carrying conditions, and view angles. Many have been presented that can effectively represent gait features. Each gait template has its advantages and can represent different prominent information. In this paper, gait template fusion is proposed to improve the classical representative gait template (such as a ) which represents incomplete information that is sensitive to changes in contour. We also present a partition method to reflect the different gait habits of different body parts of each pedestrian. The fused template is cropped into three parts (head, trunk, and leg regions) depending on the human body, and the three parts are then sent into the convolutional neural network to learn merged features. We present an extensive empirical evaluation of the CASIA-B dataset and compare the proposed method with existing ones. The results show good accuracy and robustness of the proposed method for .

Keywords： 步态识别；分块算法；步态模板；步态分析；步态能量图；深度卷积神经网络；生物特征识别；模式识别    

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A Face Recognition Based on Fusion Features Extraction From Two Kinds of Projection

Zhang Shengliang,Xu Yong,Yang Jian,Yang Jingyu

Strategic Study of CAE 2006, Volume 8, Issue 8,   Pages 50-55

Abstract:

A novel face recognition algorithm based on two kinds of projection is presented in this paper. First, the two dimension principal component analysis (2DPCA) is used to extract one group of features, denoted by α. Second, the fisher linear discriminant analysis (LDA) , or fisherfaces, is used for extracting another group of features, denoted by β.After being standardized, the two kinds of features are combined together in the form of the complex vector α+iβ. Then the fusion features in the complex feature space is extracted by using complex PCA (CPCA). The proposed algorithm is evaluated by using the FERET face database at three different resolutions. The experimental results indicate that the proposed method can achieve about 10% higher recognition accurate rate than 2DPCA and LDA, while only using 28 features for each sample.

Keywords： feature fusion     linear discriminant analysis (LDA)     feature extraction     face recognition    

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Frequency-hopping transmitter fingerprint feature recognition with kernel projection and joint representation Research Articles

Ping SUI, Ying GUO, Kun-feng ZHANG, Hong-guang LI

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 8,   Pages 1133-1146 doi: 10.1631/FITEE.1800025

Abstract: Frequency-hopping (FH) is one of the commonly used spread spectrum techniques that finds wide applications in communications and radar systems because of its inherent capability of low interception, good confidentiality, and strong antiinterference. However, non-cooperation FH transmitter classification is a significant and challenging issue for FH transmitter fingerprint feature recognition, since it not only is sensitive to noise but also has non-linear, non-Gaussian, and non-stability characteristics, which make it difficult to guarantee the classification in the original signal space. Some existing classifiers, such as the sparse representation classifier (SRC), generally use an individual representation rather than all the samples to classify the test data, which over-emphasizes sparsity but ignores the collaborative relationship among the given set of samples. To address these problems, we propose a novel classifier, called the kernel joint representation classifier (KJRC), for FH transmitter fingerprint feature recognition, by integrating kernel projection, collaborative feature representation, and classifier learning into a joint framework. Extensive experiments on real-world FH signals demonstrate the effectiveness of the proposed method in comparison with several state-of-the-art recognition methods.

Keywords： Frequency-hopping     Fingerprint feature     Kernel function     Joint representation     Transmitter recognition    

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ECGID: a human identification method based on adaptive particle swarm optimization and the bidirectional LSTM model Research Article

Yefei Zhang, Zhidong Zhao, Yanjun Deng, Xiaohong Zhang, Yu Zhang,zhangyf@hdu.edu.cn,zhaozd@hdu.edu.cn,yanjund@hdu.edu.cn,xhzhang@hdu.edu.cn,zy2009@hdu.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 12,   Pages 1551-1684 doi: 10.1631/FITEE.2000511

Abstract: Physiological signal based biometric analysis has recently attracted attention as a means of meeting increasing privacy and security requirements. The real-time nature of an electrocardiogram (ECG) and the hidden nature of the information make it highly resistant to attacks. This paper focuses on three major bottlenecks of existing deep learning driven approaches: the lengthy time requirements for optimizing the hyperparameters, the slow and computationally intense identification process, and the unstable and complicated nature of ECG acquisition. We present a novel deep neural network framework for learning feature representations directly from ECG time series. The proposed framework integrates deep bidirectional long short-term memory (BLSTM) and . The overall approach not only avoids the inefficient and experience-dependent search for hyperparameters, but also fully exploits the spatial information of ordinal local features and the memory characteristics of a recognition algorithm. The effectiveness of the proposed approach is thoroughly evaluated in two ECG datasets, using two protocols, simulating the influence of electrode placement and acquisition sessions in identification. Comparing four recurrent neural network structures and four classical machine learning and deep learning algorithms, we prove the superiority of the proposed algorithm in minimizing overfitting and self-learning of time series. The experimental results demonstrated an average identification rate of 97.71%, 99.41%, and 98.89% in training, validation, and test sets, respectively. Thus, this study proves that the application of APSO and LSTM techniques to biometric can achieve a lower algorithm engineering effort and higher capacity for generalization.

Keywords： 心电图生物特征；个体身份识别；长短期记忆网络；自适应粒子群优化算法    

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Spontaneous versus posed smile recognition via region-specific texture descriptor and geometric facial dynamics Article

Ping-ping WU, Hong LIU, Xue-wu ZHANG, Yuan GAO

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 7,   Pages 955-967 doi: 10.1631/FITEE.1600041

Abstract: As a typical biometric cue with great diversities, smile is a fairly influential signal in social interaction, which reveals the emotional feeling and inner state of a person. Spontaneous and posed smiles initiated by different brain systems have differences in both morphology and dynamics. Distinguishing the two types of smiles remains challenging as discriminative subtle changes need to be captured, which are also uneasily observed by human eyes. Most previous related works about spontaneous versus posed smile recognition concentrate on extracting geometric features while appearance features are not fully used, leading to the loss of texture information. In this paper, we propose a region-specific texture descriptor to represent local pattern changes of different facial regions and compensate for limitations of geometric features. The temporal phase of each facial region is divided by calculating the intensity of the corresponding facial region rather than the intensity of only the mouth region. A mid-level fusion strategy of support vector machine is employed to combine the two feature types. Experimental results show that both our proposed appearance representation and its combination with geometry-based facial dynamics achieve favorable performances on four baseline databases: BBC, SPOS, MMI, and UvA-NEMO.

Keywords： Facial landmark localization     Geometric feature     Appearance feature     Smile recognition    

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Study on the molecular characteristic in phlegm-dampness constitution

Wang Qi,Dong Jing,Wu Hongdong,Wang Dongpo,Yao Shilin,Ren Xiaojuan

Strategic Study of CAE 2008, Volume 10, Issue 7,   Pages 100-103

Abstract:

To investigate the molecular mechanism for phlegm-dampness constitution,examining the genome DNA of peripheral blood cells of six phlegm-dampness constitution and six normal constitution by using Affymetrix GeneChip Mapping 500K Array.We had identified 442 genes with significant differences between the phlegm-dampness constitution and the normal constitution by using Affymetrix Gene Chip Human Genome U133 plus-2, base on research results of prophase, sieve the related gene and characteristics of single nucleotide polymorphisms( SNP) of phlegm-dampnessconstitution.5 related genes and 6 SNPs with significant differences were identified between the phlegm-dampness constitution and the normal constitution. Further biologianalysis on the genes identified between two constitution groups demonstrate that they are involved in enzyme activity,sterol transporter activity,participate in the biology process of lipid metabolism, cholesterol metabolic process,brown fat cell differentiation, gluconeogenesis and thermoregulation.These results indicate that the Molecular characteristic of phlegm-dampness constitution is related to metabolism disorder.

Keywords： phlegm-dampness constitution     Genechip Mapping Array     Molecular characteristic    

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A network security entity recognition method based on feature template and CNN-BiLSTM-CRF Research Papers

Ya QIN, Guo-wei SHEN, Wen-bo ZHAO, Yan-ping CHEN, Miao YU, Xin JIN

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 6,   Pages 872-884 doi: 10.1631/FITEE.1800520

Abstract:

By network security threat intelligence analysis based on a security knowledge graph (SKG), multi-source threat intelligence data can be analyzed in a fine-grained manner. This has received extensive attention. It is difficult for traditional named entity recognition methods to identify mixed security entities in Chinese and English in the field of network security, and there are difficulties in accurately identifying network security entities because of insufficient features extracted. In this paper, we propose a novel FT-CNN-BiLSTM-CRF security entity recognition method based on a neural network CNN-BiLSTM-CRF model combined with a feature template (FT). The feature template is used to extract local context features, and a neural network model is used to automatically extract character features and text global features. Experimental results showed that our method can achieve an F-score of 86% on a large-scale network security dataset and outperforms other methods.

Keywords： Network security entity     Security knowledge graph (SKG)     Entity recognition     Feature template     Neural network    

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Histogram equalization using a reduced feature set of background speakers’ utterances for speaker recognition Article

Myung-jae KIM, Il-ho YANG, Min-seok KIM, Ha-jin YU

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 5,   Pages 738-750 doi: 10.1631/FITEE.1500380

Abstract: We propose a method for histogram equalization using supplement sets to improve the performance of speaker recognition when the training and test utterances are very short. The supplement sets are derived using outputs of selection or clustering algorithms from the background speakers’ utterances. The proposed approach is used as a feature normalization method for building histograms when there are insufficient input utterance samples. In addition, the proposed method is used as an i-vector normalization method in an i-vector-based probabilistic linear discriminant analysis (PLDA) system, which is the current state-of-the-art for speaker verification. The ranks of sample values for histogram equalization are estimated in ascending order from both the input utterances and the supplement set. New ranks are obtained by computing the sum of different kinds of ranks. Subsequently, the proposed method determines the cumulative distribution function of the test utterance using the newly defined ranks. The proposed method is compared with conventional feature normalization methods, such as cepstral mean normalization (CMN), cepstral mean and variance normalization (MVN), histogram equalization (HEQ), and the European Telecommunications Standards Institute (ETSI) advanced front-end methods. In addition, performance is compared for a case in which the greedy selection algorithm is used with fuzzy -means and -means algorithms. The YOHO and Electronics and Telecommunications Research Institute (ETRI) databases are used in an evaluation in the feature space. The test sets are simulated by the Opus VoIP codec. We also use the 2008 National Institute of Standards and Technology (NIST) speaker recognition evaluation (SRE) corpus for the i-vector system. The results of the experimental evaluation demonstrate that the average system performance is improved when the proposed method is used, compared to the conventional feature normalization methods.

Keywords： Speaker recognition     Histogram equalization     i-vector    

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A Study on the Essence of Optimal Statistical Uncorrelated Discriminant Vectors

Wu Xiaojun,Yang Jingyu,Wang Shitong,Liu Tongming,Josef Kittler

Strategic Study of CAE 2004, Volume 6, Issue 2,   Pages 44-47

Abstract:

A study has been made on the essence of optimal set of uncorrelated discriminant vectors in this paper. A whitening transform has been constructed on the basis of the eigen decomposition of population scatter matrix, which makes the population scatter matrix an identity matrix in the transformed sample space. Thus, the optimal discriminant vectors solved by conventional LDA methods are statistical uncorrelated. The research indicates that the essence of the statistical uncorrelated discriminant transform is the whitening transform plus conventional linear discriminant transform. The distinguished characteristic of the proposed method is that the obtained optimal discriminant vectors are orthogonal and statistical uncorrelated. The proposed method suits for all the problems of algebraic feature extraction. The numerical experiments on facial database of ORL show the effectiveness of the proposed method.

Keywords： pattern recognition     feature extraction     disciminant analysis     generalized optimal set of discriminant vectors     face recognition    

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Research and Development of CAD/CAPP/CAM Integrated System

Wei Erwei,Liang Muyang,Yang Ping

Strategic Study of CAE 2000, Volume 2, Issue 4,   Pages 59-63

Abstract:

In this paper a practical CAD/CAPP/CAM integrated system is introduced. This system has been developed by mechanical designers, process planners and NC progragmmers on the basis of a commercial CAD/CAM software named Pro/ENGINEER. It consists of three subsystems： Feature Recognition Subsystem, CAPP Subsystem and Automatical CAM Subsystem. In Feature Recognition Subsystem, machining features information of a part is obtained from a three dimensional part. In CAPP Subsystem, machining features information is analyzed, sets and sequences of the process planning are created automatically, tool and cutting parameters for each sequence are selected from tools library, the order of sequences is arranged. In Automatical CAM Subsystem, NC sequences for machining the part are created automatically according to the CAPP result. After the NC sequences in the machining process being simulated, the file of cutter location data is generated. Finally through a post processor, the NC code file is output and transmitted to a NC machine to machine the part.

Keywords： CAD     CAPP     CAM     feature recognition    

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Passive millimeter-wave target recognition based on Laplacian eigenmaps

Luo Lei,Li Yuehua,Luan Yinghong

Strategic Study of CAE 2010, Volume 12, Issue 3,   Pages 77-81

Abstract:

Aiming at the disadvantages of feature extraction and selection in the traditional method for passive millimeter-wave (MMW) metal target recognition, the existence and characteristics of low dimensional manifold of the short-time Fourier spectrum of metal target echo signal are explored using manifold learning algorithm, Laplacian eigenmaps. Target classification is performed through comparing the similarity of the test samples and the positive class in terms of the low dimensional manifold. The experiments show that the method gets higher recognition rate than other linear and kernel-based nonlinear dimensionality reduction algorithm, and is robust to data aliasing.

Keywords： manifold learning     Laplacian eigenmaps     nonlinear dimensionality reduction     low dimensional manifold     MMW    

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DDUC: an erasure-coded system with decoupled data updating and coding Research Article

Xiang LI, Yibing LI, Chunrui TANG, Yingsong LI,chunruitang@126.com

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 5,   Pages 742-758 doi: 10.1631/FITEE.2200253

Abstract: To improve the accuracy of modulated signal recognition in variable environments and reduce the impact of factors such as lack of prior knowledge on recognition results, researchers have gradually adopted deep learning techniques to replace traditional modulated signal processing techniques. To address the problem of low recognition accuracy of the modulated signal at low signal-to-noise ratios, we have designed a novel network of multi-scale analysis with deep threshold noise elimination to recognize the actually collected modulated signals under a symmetric cross-entropy function of label smoothing. The network consists of a denoising encoder with deep adaptive threshold learning and a decoder with . The two modules are skip-connected to work together to improve the robustness of the overall network. Experimental results show that this method has better recognition accuracy at low signal-to-noise ratios than previous methods. The network demonstrates a flexible self-learning capability for different noise thresholds and the effectiveness of the designed feature fusion module in multi-scale feature acquisition for various modulation types.

Keywords： Signal noise elimination     Deep adaptive threshold learning network     Multi-scale feature fusion     Modulation recognition    

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Characterization of the Gastric Mucosal Microbiota in Patients with Liver Cirrhosis and Its Associations with Gastrointestinal Symptoms Article

Yanfei Chen, Jing Guo, Chunlei Chen, Ding Shi, Daiqiong Fang, Feng Ji, Lanjuan Li

Engineering 2021, Volume 7, Issue 4,   Pages 507-514 doi: 10.1016/j.eng.2020.04.014

Abstract:

Several studies have indicated that the oral and gut microbiota may exhibit differences in patients with cirrhosis. Less is known about the microbiota in the stomach, which is located between the oral cavity and the intestinal tract. In this study, the gastric mucosal microbiota of patients with liver cirrhosis and controls were analyzed with 16S ribosomal RNA (rRNA) pyrosequencing. Cirrhotic patients had significantly
lower Helicobacter pylori (H. pylori) infection rates, as confirmed by both the histological method and the pyrosequencing method. In H. pylori-negative subjects, gastric bacterial communities of healthy and cirrhosis cohorts were clustered into four clusters based on bacterial compositions: Cluster_1 and Cluster_2 (mostly cirrhosis), Cluster_3 (mostly healthy), and Cluster_4 (around half of each). Compositional and functional differences were observed among these different clusters. At the genus level, Cluster_1 and Cluster_2 showed enrichment of Neisseria and Streptococcus, respectively. Functionally, Cluster_2 was characterized as depleted of genetic information processing, as well as of modules related to glycan biosynthesis and metabolism. Patients in Cluster_2 had more severe gastrointestinal symptoms and a higher rate of previous endoscopic variceal ligation (EVL) therapy than patients in other clusters. Our findings suggest that the colonization of both H. pylori and non-H. pylori is influenced in liver cirrhosis. Although the H. pylori-negative gastric mucosal microbiota showed considerable heterogeneity, associations between specific gastric microbiota and clinical characteristics could be observed. Previous EVL therapy might lead to a distinct structure of the gastric mucosal microbiota, thus aggravating the gastrointestinal symptoms in H. pylori-negative cirrhotic patients.

Keywords： Microbiome     Liver cirrhosis     Symptoms     Varices     Gastric endoscopy    

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The expression mode of hsa-miR-197 in uterine leiomyomas tissue and related bioinformatics analysis

Xu Qing,Fu Ziyi,Wu Xiaoli,Huangfu Yushuang and Ling Jing

Strategic Study of CAE 2014, Volume 16, Issue 5,   Pages 99-104

Abstract:

We performed Real-time PCR to detect the hsa-miR-197 expression levels in uterine leiomyomas tissues and paired normal myometrium separately；online databases including miRbase，UCSC，NCBI are employed to analysis the sequence conservation and genetic characteristics of hsa- miR- 197；miRNA databases such as miRanda，MirTarget2 and TargetScan are chosen to predict hsa- miR- 197 target genes；meanwhile，the intersection genes of these miRNA databases are chosen，and GO and Pathway analysis of these genes are carried out to explore the potential role of hsa-miR-197 playing in regulating the uterine leiomyomas occurrence and development. hsa-miR-197 functions in broad ground and participates in uterine leiomyomas multiple biology progress，which indicates that hsa- miR- 197 could regulate uterine leiomyomas occurrence and development.

Keywords： uterine leiomyomas     hsa-miR-197     bioinformatics analysis     target genes    

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A personality-guided affective brain–computer interface for implementation of emotional intelligence in machines Research Article

Shaojie LI, Wei LI, Zejian XING, Wenjie YUAN, Xiangyu WEI, Xiaowei ZHANG, Bin HU,zhangxw@lzu.edu.cn,bh@lzu.edu.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 8,   Pages 1158-1173 doi: 10.1631/FITEE.2100489

Abstract: Affective brain–computer interfaces have become an increasingly important topic to achieve emotional intelligence in human–machine collaboration. However, due to the complexity of signals and the individual differences in emotional response, it is still a great challenge to design a reliable and effective model. Considering the influence of on emotional response, it would be helpful to integrate personality information and EEG signals for . This study proposes a personality-guided attention neural network that can use personality information to learn effective EEG representations for . Specifically, we first use a convolutional neural network to extract rich temporal and regional representations of EEG signals, and a special convolution kernel is designed to learn inter- and intra-regional correlations simultaneously. Second, inspired by the fact that electrodes within distinct brain scalp regions play different roles in , a personality-guided regional- is proposed to further explore the contributions of electrodes within a region and between regions. Finally, attention-based long short-term memory is designed to explore the temporal dynamics of EEG signals. Experiments on the AMIGOS dataset, which is a dataset for multimodal research for affect, , and mood on individuals and groups, show that the proposed method can significantly improve the performance of subject-independent and outperform state-of-the-art methods.

Keywords： Electroencephalogram (EEG)     Emotion recognition     Attention mechanism     Personality traits    

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Title Author Date Type Operation

A partition approach for robust gait recognition based on gait template fusion

Kejun Wang, Liangliang Liu, Xinnan Ding, Kaiqiang Yu, Gang Hu,heukejun@126.com,liuliangliang@hrbeu.edu.cn,dingxinnan@hrbeu.edu.cn,yukaiqiang@hrbeu.edu.cn,hugang@hrbeu.edu.cn

Journal Article

A Face Recognition Based on Fusion Features Extraction From Two Kinds of Projection

Zhang Shengliang,Xu Yong,Yang Jian,Yang Jingyu

Journal Article

Frequency-hopping transmitter fingerprint feature recognition with kernel projection and joint representation

Ping SUI, Ying GUO, Kun-feng ZHANG, Hong-guang LI

Journal Article

ECGID: a human identification method based on adaptive particle swarm optimization and the bidirectional LSTM model

Yefei Zhang, Zhidong Zhao, Yanjun Deng, Xiaohong Zhang, Yu Zhang,zhangyf@hdu.edu.cn,zhaozd@hdu.edu.cn,yanjund@hdu.edu.cn,xhzhang@hdu.edu.cn,zy2009@hdu.edu.cn

Journal Article

Spontaneous versus posed smile recognition via region-specific texture descriptor and geometric facial dynamics

Ping-ping WU, Hong LIU, Xue-wu ZHANG, Yuan GAO

Journal Article

Study on the molecular characteristic in phlegm-dampness constitution

Wang Qi,Dong Jing,Wu Hongdong,Wang Dongpo,Yao Shilin,Ren Xiaojuan

Journal Article

A network security entity recognition method based on feature template and CNN-BiLSTM-CRF

Ya QIN, Guo-wei SHEN, Wen-bo ZHAO, Yan-ping CHEN, Miao YU, Xin JIN

Journal Article

Histogram equalization using a reduced feature set of background speakers’ utterances for speaker recognition

Myung-jae KIM, Il-ho YANG, Min-seok KIM, Ha-jin YU

Journal Article

A Study on the Essence of Optimal Statistical Uncorrelated Discriminant Vectors

Wu Xiaojun,Yang Jingyu,Wang Shitong,Liu Tongming,Josef Kittler

Journal Article

Research and Development of CAD/CAPP/CAM Integrated System

Wei Erwei,Liang Muyang,Yang Ping

Journal Article

Passive millimeter-wave target recognition based on Laplacian eigenmaps

Luo Lei,Li Yuehua,Luan Yinghong

Journal Article

DDUC: an erasure-coded system with decoupled data updating and coding

Xiang LI, Yibing LI, Chunrui TANG, Yingsong LI,chunruitang@126.com

Journal Article

Characterization of the Gastric Mucosal Microbiota in Patients with Liver Cirrhosis and Its Associations with Gastrointestinal Symptoms

Yanfei Chen, Jing Guo, Chunlei Chen, Ding Shi, Daiqiong Fang, Feng Ji, Lanjuan Li

Journal Article

The expression mode of hsa-miR-197 in uterine leiomyomas tissue and related bioinformatics analysis

Xu Qing,Fu Ziyi,Wu Xiaoli,Huangfu Yushuang and Ling Jing

Journal Article

A personality-guided affective brain–computer interface for implementation of emotional intelligence in machines

Shaojie LI, Wei LI, Zejian XING, Wenjie YUAN, Xiangyu WEI, Xiaowei ZHANG, Bin HU,zhangxw@lzu.edu.cn,bh@lzu.edu.cn

Journal Article