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检索条件"主题词=detection algorithms"

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MixNet for Generalized Face Presentation Attack detection

MixNet for Generalized Face Presentation Attack Detection

引用

International Conference on Pattern Recognition

作者： Nilay Sanghvi Sushant Kumar Singh Akshay Agarwal Mayank Vatsa Richa Singh IIIT-Delhi India Texas A&M University Kingsville USA IIT Jodhpur India

The non-intrusive nature and high accuracy of face recognition algorithms have led to their successful deployment across multiple applications ranging from border access to mobile unlocking and digital payments. However, their vulnerability against sophisticated and cost-effective presentation attack mediums raises essential questions regarding its reliability. In the literature, several presentation attack detection algorithms are presented; however, they are still far behind from reality. The major problem with existing work is the generalizability against multiple attacks both in the seen and unseen setting. The algorithms which are useful for one kind of attack (such as print) perform unsatisfactorily for another type of attack (such as silicone masks). In this research, we have proposed a deep learning-based network termed as MixNet to detect presentation attacks in cross-database and unseen attack settings. The proposed algorithm utilizes state-of-the-art convolutional neural network architectures and learns the feature mapping for each attack category. Experiments are performed using multiple challenging face presentation attack databases such as SMAD and Spoof In the Wild (SiW-M) databases. Extensive experiments and comparison with existing state of the art algorithms show the effectiveness of the proposed algorithm.

关键词： Databases Face recognition Distance measurement Reliability Convolutional neural networks detection algorithms

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CatBoost for Fraud detection in Financial Transactions

CatBoost for Fraud Detection in Financial Transactions

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Consumer Electronics and Computer Engineering (ICCECE), 2021 IEEE International Conference on

作者： Yeming Chen Xinyuan Han ClarityAI Beijing China

ISBN: (数字)9781728183190

ISBN: (纸本)9781728183206

Financial fraud is an ever growing menace with severe consequences in the financial industry. Machine learning plays an active role in the fraud detection in financial transactions. However, fraud detection is still a challenging problem due to two major reasons. First, either fraudulent or non-fraudulent behaviors change fast and constantly. Secondly, currently online transactions happen so fast, which require detection algorithms to be efficient and accurate. This paper introduces a machine learning method based on CatBoost for fraud detection. To improve detection accuracy, we apply feature engineering to generate highly important features and feed them into CatBoost for classification. Another key contribution of our work is using memory compression to speed up detection. The performance of our method is evaluated on a publicly IEEE-CIS Fraud dataset, provided by Kaggle competition platform. The experimental results demonstrate that our model based on CatBoost has obtained optimal accuracy of 0.983.

关键词： Knowledge engineering Machine learning Feature extraction Feeds detection algorithms Financial industry Consumer electronics

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Study of Dynamic Geometrical Parameter detection System for Tramcar Track Irregularity

Study of Dynamic Geometrical Parameter Detection System for ...

引用

Consumer Electronics and Computer Engineering (ICCECE), 2021 IEEE International Conference on

作者： Hongru Fan Zhichao He Jiabin Huang Chen Li Yi Yin Yong Jun Xie School of Electrical Engineering Jinan University Rail Transit Research Institute Jinan University Zhu Hai China

ISBN: (数字)9781728183190

ISBN: (纸本)9781728183206

Despite the rapid development of the tramcar market, the study concerning security detection has been a later starter and mostly remains the human detection stage currently, resulting in low detection efficiency and precision. In this case, the paper suggests a detection system for the track irregularity of tramcars, realizing non-contact dynamic measurement over the geometric parameters for groove track longitudinal level and alignment irregularity. In light of this, it further proposes an irregularity detection algorithm for the geometric parameters of groove tracks based on the principle of laser distance measuring and the mid-chord offset (MCO) method, which overcomes the traditional asymmetrical chord offset method's defect of being only capable of checking the irregularity of short wavelengths within lm and avoids the damage of tracks due to contact track measuring instruments. Importantly, the high detection accuracy, favorable stability, and other favorable characteristics of the detection system proposed have been proven by experimental verification, thereby providing the detection of geometric parameters for modern tramcars with a new method and a new tool.

关键词： Wavelength measurement Heuristic algorithms Tools Laser stability Distortion measurement detection algorithms Standards

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RX infrared small target detection algorithm based on multi-scale local strength and gradient fusion

RX infrared small target detection algorithm based on multi-...

引用

Power Electronics, Computer Applications (ICPECA), IEEE International Conference on

作者： Jing Tian Dan Song Yingguang Hao Weiwei Yin Rui Li Ke Han School of Information and Communication Engineering Dalian University of Technology Dalian China Northeast Branch Corporation State Grid Corporation of China Shenyang China Maintenance Company State Grid Jilisheng Electric Power Supply Company Changchun China

ISBN: (数字)9781728190044

ISBN: (纸本)9781728190051

Infrared small target detection under complex background is still a challenging computer vision task. An image usually contains complex background, weak targets and chaotic noise. In order to solve this problem, we must try our best to enhance the target signal to noise ratio (SNR) while suppressing the complex background factors. We proposed an RX infrared small target detection algorithm by analyzing multi-scale local gradient and local intensity fusion feature. According to the gradient points to the target center and the intensity value of the target pixel is greater than the intensity value of its local adjacent pixels, We used multi-scale information to calculate the local gradient map and local intensity map of infrared image and used RX global detection method to extract small targets. The experiment shows, our algorithm has good performance on detection result. It has good robustness and effectiveness, which can be applied to infrared small target detection under complex background.

关键词： Target tracking Object detection Robustness Data mining detection algorithms Task analysis Signal to noise ratio

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An Overlapping Community detection Algorithm based on Density Peaks and Label Propagation

An Overlapping Community Detection Algorithm based on Densit...

引用

International Conference on Intelligent System and Knowledge Engineering, ISKE

作者： Chuanwei Li Hongmei Chen Tianrui Li School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China

ISBN: (纸本)9781665405546

Many nodes in real networks belong to multiple communities at the same time. The research methods of overlapping community detection have attracted more and more attention. Density peak clustering (DPC) and label propagation algorithm (LPA) can efficiently complete community detection. However, DPC cannot distinguish overlapping nodes in the network, and the stability of LPA is very poor. In this paper, we propose an overlapping community detection algorithm based on density peaks and label propagation (ODLPA). ODLPA uses the degree of neighbor nodes to calculate the local density of nodes and expands the selection range of community centers. At the same time, ODLPA uses and improves the Speaker-listener rule to stably and efficiently detect overlapping communities in the initial community. ODLPA also proposed a community integration method based on greedy strategy to further improve the quality of community division. Finally, the effectiveness of the proposed algorithm is verified by comparison with other related algorithms on real-world network.

关键词： Knowledge engineering Clustering algorithms Data mining detection algorithms Intelligent systems Thermal stability

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Comparison of the Deep Learning Methods Applied on Human Eye detection

Comparison of the Deep Learning Methods Applied on Human Eye...

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Power Electronics, Computer Applications (ICPECA), IEEE International Conference on

作者： Yunjie Xiang Haiyan Yang Rong Hu Chih-Yu Hsu School of Computer Science and Mathematics Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Automotive Electronics and Electric Drive Fujian University of Technology Fuzhou China

ISBN: (数字)9781728190044

ISBN: (纸本)9781728190051

For the fatigue driving detection of a driver wearing a mask, the traditional fatigue driving detection method cannot effectively detect the face. The characteristics of the mouth area are disappeared due to the mask's occlusion. Therefore, the extraction of fatigue features in the eye area becomes very important. The accuracy of the eye area detection will directly affect the performance of the fatigue driving detection algorithm. At present, YOLOv3 and Faster-RCNN are both excellent models in the field of target detection. Therefore, this article uses the same data set and sets the same training parameters during training. Under a unified evaluation standard, the YOLOv3 model and the Faster-RCNN model are evaluated. Experimental results show that YOLOv3 has a better effect on human eye detection under the same conditions.

关键词： Training Object detection Fatigue Feature extraction detection algorithms Task analysis Standards

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Surface Defect detection Based on Deep Learning and Collaborative Cloud-edge Computation

Surface Defect Detection Based on Deep Learning and Collabor...

引用

International Conference on Computer and Communications (ICCC)

作者： Shunjie He Bo Yang Dafeng Zhu Cheng Li Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Industrial Intelligent Control and Management Shanghai China

ISBN: (纸本)9781665409513

Surface defect detection is a fundamental step of quality management. Nowadays, surface defect detection system based on machine vision technology has made great progress which can gradually replace the human inspector. With the help of convolutional neural networks, the accuracy and stability of the surface defect detection algorithm are improving rapidly. However, it is difficult to process the images which have small defect areas and complex backgrounds. Furthermore, lacking defective samples and limited computation resources are also tough problems. To deal with these problems, a two-stage algorithm combined with object detection and classification networks is proposed. Transform learning and data augmentation are used to deal with the problem of lacking defect samples. Besides, a detection system based on collaborative cloud-edge computation is designed to improve the performance of the algorithm. The result shows that the proposed method has high performance and low requirements of computation resources.

关键词： Training Collaboration Transforms Object detection Classification algorithms Convolutional neural networks detection algorithms

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EML-Based IM/DD Transmission over Dispersion-Uncompensated Links Using Low-Complexity Optimized detection

EML-Based IM/DD Transmission over Dispersion-Uncompensated L...

引用

Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Shuangyue Liu Han Cui Xizi Tang Yueming Lu Yaojun Qiao State Key Laboratory of Information Photonics and Optical Communications School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China

We experimentally demonstrate an EML-based 40-Gbit/s OOK IM/DD trans-mission over 120-km SSMF using a low-complexity optimized detection algorithm, which requires no multiplication during the update of survival path, ... 详细信息

ISBN: (纸本)9781957171005

关键词： Chromatic dispersion Asia Optical fiber communication Complexity theory detection algorithms Photonics

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Application of Primary Component Analysis and Feature Map fusion to Improve Real-time Prostate Capsula detection

Application of Primary Component Analysis and Feature Map fu...

引用

IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

作者： Shixiao Wu Chengzhang Qu College of Information Engineering Wuhan Business University Wuhan China

ISBN: (数字)9781728180281

ISBN: (纸本)9781728180298

This study investigated the potential for using principal component analysis (PCA) and feature map fusion to improve real-time prostate cpasula detection algorithms. Some objective detection algorithms can realize real-time object detection, but the mean average precision (mAP) is not ideal. PCA is processed firstly to make dimensionality reduction. After that, the processed images are sent to the new network called Almost Fully Feature Fusion Single Shot Multibox Detector (AFFSSD) to train. Feature map size at 150×150, 75×75, 38×38, 19×19, 10×10, 5×5, 3×3, 1×1 are all utilized for feature collection by feature map fusion and feature pyramid therein. Results showed that PCA and AFFSSD has the highest mAP for the prostate capsula images detection, by 76.23%, compared with Faster R-CNN, R-FCN, YOLOv3, SSD, FSSD applied independently. The proposed architecture fused many low-level features, so the confidence score are very high, almost 100 percent.

关键词： Dimensionality reduction Object detection Feature extraction Real-time systems detection algorithms Information technology Principal component analysis

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Research on the structure of heterogeneous combat network based on improved Hete _ M community detection algorithm

Research on the structure of heterogeneous combat network ba...

引用

International Conference on Electronics, Communications and Information Technology (CECIT)

作者： Liang Guo Yunjun Lu Kebin Chen Lvjun Zhao Qichao Peng College of Information and Communication National University of Defense Technology Wuhan China

ISBN: (纸本)9781665437585

The research on the structural characteristics of Heterogeneous combat network (HCN) can provide an effective basis for its scientific organization and guarantee. The community structure analysis based on complex network can provide a powerful method for the research on the structural characteristics of HCN. In view of this, this paper put forward the idea of studying the structural characteristics of HCN based on community structure, and improve Hete_M community detection algorithm. In the experiment, different functional nodes are used as seeds to detect the community structure, which can provide reference for the node configuration of different functional elements. Meanwhile, the results show that the effect of dividing HCN structure with reconnaissance perception nodes as seeds is better, which provide a new idea for organizing HCN with reconnaissance perception elements as the core.

关键词： Organizations Complex networks Reconnaissance Heterogeneous networks Information and communication technology detection algorithms Task analysis

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