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Enhancing Security with Machine Learning-based Finger-Vein Biometric Authentication System 5

Enhancing Security with Machine Learning-based Finger-Vein B...

引用

5th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2024

作者： Babu, G. Prakash Aswal, Upendra Singh Sindhu, M. Upadhye, Vijay Jagdish Natrayan, L. Patil, Harshal Acharya Institute of Technology Department of Computer Science and Engineering Karnataka Bangalore560 107 India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India R.M.K. Engineering College Department of Computer Science and Business Systems Tamil Nadu Thiruvallur601 206 India Parul Institute of Applied Sciences Research and Development Cell Parul University Department of Microbiology Gujarat Waghodia391760 India SIMATS Saveetha School of Engineering Department of Mechanical Engineering Tamil Nadu Chennai602105 India Computer Science and Engineering Symbiosis Institute of Technology Maharashtra Pune412 115 India

ISBN: (纸本)9798350395235

Biometric authentication systems have gained popularity as the necessity for secure individual identification has expanded. Iris, hand geometry, fingerprints, retina, vein patterns on the fingers and palms, and voice patterns are all common biometric features. Biometric recognition systems based on finger-veins are becoming more common due to their numerous advantages, including low danger of duplication or fraud, minimum invasiveness, and excellent identification accuracy. Motivated by these advantages, our research looks into the subject of finger-vein authentication. Similar to identification, exploration of hidden patterns within data is essential for effective finger-vein recognition. The study employs two feature extraction approaches, the Local Binary pattern (LBP) and the Gabor filter, as well as two machine learning models, the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Support Vector Machine (SVM). Using finger-vein images from the SDUMLA HMT database, this study evaluates four distinct feature extraction and ML model combinations. The simulation results highlight the excellent accuracy of 97.72%, False Acceptance Rate (FAR) of 3.1%, and False Rejection Rate (FRR) of 1.41% attained by LBP with ANFIS. In addition to performance measurements, the study examines the amount of time required for each task. © 2024 IEEE.

关键词： Local binary pattern

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Learning to Optimize Quantum Neural Networks Without Gradients 4

Learning to Optimize Quantum Neural Networks Without Gradien...

引用

4th IEEE international conference on Quantum computing and Engineering, QCE 2023

作者： Kulshrestha, Ankit Liu, Xiaoyuan Ushijima-Mwesigwa, Hayato Safro, Ilya Newark United States Fujitsu Research of America Sunnyvale United States

ISBN: (纸本)9798350343236

Quantum Machine Learning is an emerging sub-field in machine learning where one of the goals is to perform pattern recognition tasks by encoding data into quantum states. This extension from classical to quantum domain has been made possible due to the development of hybrid quantum-classical algorithms that allow a parameterized quantum circuit to be optimized using gradient based algorithms that run on a classical computer. The similarities in training of these hybrid algorithms and classical neural networks has further led to the development of Quantum Neural Networks (QNNs). However, in the current training regime for QNNs, the gradients w.r. t objective function have to be computed on the quantum device. This computation is highly non-scalable and is affected by hardware and sampling noise present in the current generation of quantum hardware. In this paper, we propose a training algorithm that does not rely on gradient information. Specifically, we introduce a novel meta-optimization algorithm that trains a meta-optimizer network to output parameters for the quantum circuit such that the objective function is minimized. We empirically and theoretically show that we achieve a better quality minima in fewer circuit evaluations than existing gradient based algorithms on different datasets. © 2023 IEEE.

关键词： pattern recognition

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Temporal Contrast Sets Mining 1

引用

5th international conference on soft computing and its Engineering Applications (IcsoftComp)

作者： Orabi, Mariam Al Aghbari, Zaher Univ Sharjah Comp Sci Dept Sharjah U Arab Emirates

ISBN: (数字)9783031537318

ISBN: (纸本)9783031537301;9783031537318

Discovering discriminating features' values that distinguish different data groups is vital for understanding the unique characteristics that define each group. These distinct features can be utilized in various applications, such as classification and data mining. One effective technique for this task is Contrast Sets Mining, which identifies sets of attribute-value pairs that differentiate groups. However, existing contrast sets mining techniques overlook the temporal dimension of data, which is critical in certain applications like disease identification based on ordered symptoms. To address this limitation, this work introduces a novel approach called Temporal Contrast Sets Mining, which leverages sequential association rules to capture the temporal aspect of the data. The proposed model is evaluated using a real dataset of students' academic performance. The results are discussed, providing valuable insights for educators and students to gain a better understanding of the significant sequences that influence students' performance. By incorporating the temporal dimension into contrast sets mining, this research contributes to the advancement of techniques for analyzing ordered data, thereby enhancing the applicability of contrast sets in various domains.

关键词： Data mining pattern discovery Contrast sets Temporal association rules Sequential dataset

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Handcrafted and Deep Features for Synthetic Data Generation in Offline Handwritten Signature Verification 4th

Handcrafted and Deep Features for Synthetic Data Generation ...

引用

4th international conference on Intelligent Systems and pattern recognition-ISPR

作者： Arab, Naouel Nemmour, Hassiba Bouibed, Mohamed Lamine Chibani, Youcef Univ Sci & Technol Houari Boumediene USTHB Lab Ingn Syst Intelligents & Commun LISIC Fac Elect Engn Algiers Algeria

ISBN: (纸本)9783031821554;9783031821561

One of the major limitations which obstruct the development of an efficient offline signature verification system is the lack of genuine signatures required to perform a reliable training stage. To deal with this issue, data augmentation techniques are employed to add synthetic genuine signatures. Presently, the signature verification system is developed by applying an algorithm for synthetic features generation. We evaluate the effectiveness of this feature generator with two different signature descriptors. The first descriptor is the Local Directional Strength patterns while the second descriptor is based on the SigNet deep features. The verification phase is carried out with an SVM classifier. Experiments are performed on CEDAR and MCYT-75 datasets according to the writer-dependent verification approach. The inclusion of synthetic features considerably enhances the system performance, yielding a gain of 17% when the verification system employs only one real signature. Compared to handcrafted features, the SigNet provide more effective synthetic features with both datasets.

关键词： Artificial Immune System Handwritten Signature Verification Local Directional Strength pattern SigNet features Synthetic feature generation

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An Image Feature Extraction Algorithm Based on Tissue P System

An Image Feature Extraction Algorithm Based on Tissue P Syst...

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2023 international conference on Applied Mathematics and Digital Simulation, AMDS 2023

作者： Huang, Yourui Song, Hongping Han, Tao Xu, Shanyong Jiang, Lingya Anhui University of Science and Technology Huainan232001 China West Anhui University Lu'an237012 China

As digital images continue to generate an increasing amount of data, image feature extraction has become a crucial component of image recognition. This paper proposes an image feature extraction method based on membrane computing to extract image features. The author first uses the rotation invariant local phase quantization (RILPQ) to extract image features and combines the tissue P system with the binary particle swarm optimization (MBPSO) to select the best image features and maximize the classification accuracy. Based on 4 public datasets, 28 datasets are newly constructed, and the proposed method is verified on 28 datasets. Specifically, firstly, local binary pattern (LBP) algorithm and RILPQ are used to extract image features, and then MBPSO, binary particle swarm optimization (BPSO), genetic algorithm (GA) and membrane genetic algorithm (MGA) are used to select the optimal features. The experimental results demonstrate that our proposed image feature extraction method achieves high classification accuracy, stability, and convergence. © Published under licence by IOP Publishing Ltd.

关键词： Local binary pattern

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Individual Identification of QPSK Modulation Communication Radiator Based on ResNet-18 2

Individual Identification of QPSK Modulation Communication R...

引用

2nd international conference on Machine Learning, Cloud computing, and Intelligent Mining, MLCCIM 2023

作者： Peng, Runlong Gao, Hang Long, Han College of Electronic Science National University of Defense Technology Changsha China College of Science National University of Defense Technology Changsha China

ISBN: (纸本)9798350328530

With the increasing number of wireless communication devices, the electromagnetic spectrum is becoming increasingly crowded. In order to enhance the ability in monitoring the electromagnetic spectrum and improve the efficiency of spectrum utilization, it is necessary to strengthen the research on the individual identification technology of the communication radiation source. To solve the problem of individual identification of QPSK modulated communication radiators, a deep learning method is introduced. The input of residual neural network is signal spectrum and time domain sampling, and the individual number of radiators is used as label for individual identification of radiators. © 2023 IEEE.

关键词： pattern recognition

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Enhanced Camouflage Detection Using Advanced Image Processing Techniques for Real-Time Object recognition and pattern Differentiation 5

Enhanced Camouflage Detection Using Advanced Image Processin...

引用

5th international conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Manikandan, G. Deepa, R. Jayalakshmi, V. Thilakavathy, P. Surendran, R. St. Joseph's College of Engineering Anna University Department of Artificial Intelligence and Machine Learning Chennai India School of Computing SRM Institute of Science and Technology Department of Computing Technologies Chennai India SRM Institute of Science and Technology Department of Computer Science and Applications Chennai India Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

The identification of objects that blend in with their surroundings has long been a concern in fields including defence, wildlife monitoring, and surveillance due to the difficulty of detecting such objects. This finding is significant because it could greatly enhance our capacity to detect camouflaged objects, transforming the field of real-time object detection. Existing image processing methods often fail miserably when adequately capturing this aspect. This dissertation's primary focus is identifying devices with complicated patterns, legacy interference, and inadequate contrast;nonetheless, in disguised scenarios, all of these features cause identification to be unsuccessful. This research often suggests a new approach called the Linear Edge Detecting Scheme for Camouflaged Object Detection (LEDS-COD) in these difficult cases. The LEDS-COD system can enhance item visibility in low-comparison settings by highlighting critical edges and descriptions. pattern differentiation methodologies and modern facility-side recognition techniques are combined to accomplish this goal. The approach successfully differentiated items because it zeroed in on texture and comparative details that would otherwise go unnoticed by humans or advanced algorithms. LEDS-COD is useful in many areas, including the Navy, autonomous surveillance systems, and animal protection, where the real-time identification of hidden devices is essential. Results from a simulation study using various datasets, including concealed objects, show that LEDS-COD surpasses conventional detection methods in recognition accuracy, processing speed, and perseverance. The recommended technique exceeded the requirements for obtaining improved object detection estimates for challenging conditions like those with complex backdrops and changing illumination. According to this study, LEDS-COD can be devastating in improving real-time camouflage detection. © 2024 IEEE.

关键词： Accuracy Image processing Surveillance Image edge detection Wildlife Lighting Object detection Real-time systems Object recognition Protection

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Application of Traditional pattern recognition Algorithm in Cultural and Creative Product Design and System Development

Application of Traditional Pattern Recognition Algorithm in ...

引用

2022 international conference on Sustainable computing and Data Communication Systems, ICSCDS 2022

作者： Xu, Nan Zhengzhou University of Economics and Business Zhengzhou Henan 450000 China

ISBN: (纸本)9781665478847

This paper studies the application of traditional graphic pattern recognition algorithms in cultural and creative product design and system development. First, the application of modern design principles of formal beauty and selection, replacement and reconstruction, deconstruction and reorganization, color psychology and emotional communication and other design methods. And use image recognition algorithms to extract and recognize the characteristics of traditional patterns. When deconstructing and reorganizing, you can carefully study the color application psychology of traditional culture, and add some interesting changes to make it unique in the design of cultural and creative products. Beautiful feelings. Research on the design and development of Juchao's cultural and creative products from the perspectives of design concepts, drawings, and materials. © 2022 IEEE.

关键词： Product design

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Research on pattern recognition of English Online Discussion Text Mining Data Based on Corpus

Research on Pattern Recognition of English Online Discussion...

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Cybersecurity, Internet of Things and soft computing (CITSC), Asia-Europe conference on

作者： Yanli Ge Changchun University of Architecture and Civil Engineering Changchun China

ISBN: (数字)9798331504205

ISBN: (纸本)9798331504212

This paper studies a pattern recognition method for English online discussion text mining data based on corpus, aiming to reveal the main language patterns and potential information structures in online discussions. By constructing a special English online discussion corpus, the paper deeply processes a large amount of text data and use text mining technology and pattern recognition algorithms to extract core features. The system design includes three modules: data preprocessing, feature extraction, and pattern classification. Among them, the data preprocessing module mainly completes data cleaning and standardization; the feature extraction module uses word frequency-inverse document frequency (TF-IDF) and word embedding technology (such as Word2Vec) to obtain high-dimensional semantic features; the pattern classification module uses a combination of convolutional neural network (CNN) and long short-term memory network (LSTM) to accurately classify text data. To verify the effectiveness of the algorithm, the paper conducted simulation experiments on the constructed corpus. The results show that the algorithm proposed in this study is superior to traditional methods in both accuracy and recall of pattern recognition. In multiple experiments, the average accuracy of pattern recognition reached 92.3% and the recall rate reached 89.7%, which significantly improved the accuracy of pattern recognition.

关键词： Text mining Accuracy Semantics Data preprocessing pattern classification Feature extraction Classification algorithms Convolutional neural networks Long short term memory System analysis and design

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Fruit recognition Through Active Grasping of Two-Fingered Gripper Using a soft Skin Sensor

Fruit Recognition Through Active Grasping of Two-Fingered Gr...

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2023 international conference on Radar, Antenna, Microwave, Electronics, and Telecommunications, ICRAMET 2023

作者： Mahendra, Oka Arifin, Muhammad Pratama, Rian Putra Munandar, Aris Baskoro, Catur Hilman A.H.B. Yulianti, Lista Eka Sakti, Indra Muhtadin Research Center for Smart Mechatronics National Research and Innovation Agency Bandung Indonesia Research Center for Appropriate Technology National Research and Innovation Agency Subang Indonesia Research Center for Electronics National Research and Innovation Agency Serpong Indonesia Sepuluh Nopember Institute of Technology Department of Computer Engineering Surabaya Indonesia

ISBN: (纸本)9798350343892

Recognizing the fruit is a critical function to achieve for an autonomous harvesting system. Since fruit commonly grows in complex environments, it is challenging for the vision system to accurately identify the fruit. Humans, on the other hand, can easily recognize fruits by using their hands only. This paper proposes a method for fruit recognition based on active grasping with two-fingered grippers equipped with a soft skin sensor. The soft skin sensor has a soft silicone that is attached to the fingertip gripper. This sensor provides a 3-axis in normal and shear force based on a hall effect sensor. Active grasping by squeezing was implemented to sense and record time-series data of fruit properties. The evaluation was performed using several machine learning classification methods, such as Random Forest, Support Vector Machines (SVM), and K-Neighbors. The gripper can recognize fruit types with 88% accuracy using the Random Forest classifier. These results confirm the gripper's effective fruit recognition ability using active grasping to mimic human hand capabilities. © 2023 IEEE.

关键词： Fruits

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