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检索条件"任意字段=Conference on Biometric Technology for Human Identification"

共 292 条记录，以下是1-10 订阅

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Bimodal biometric human Recognition System Using Deep Learning Algorithm 2

Bimodal Biometric Human Recognition System Using Deep Learni...

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2nd International conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Vasavi, J. Chidambaranathan, P. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India B.S.Abdur Rahman Crescent Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798331505745

biometric recognition system combining two or more traits is a method of identifying human using deep learning techniques. Protection is the primary issue for humans nowadays. biometric human identification system using single trait may not be accurate due to inconsistencies in the captured data, making system more vulnerable to spoofing or hacking and also sensitive to environmental conditions. Recently, Multimodal biometric techniques help mitigate these issues by providing high security and more accuracy. This research work combines two traits such as iris and palm print to encounter the growing demand for high precision in today's biometric system known as Bimodal biometric system. Bimodal systems are working to extend the high performance which will not be possible using single trait. This paper reviews the background of iris and palm print, then apply preprocessing techniques to remove noise and classification using Convolutional Neural Network (CNN) for human identification. © 2025 IEEE.

关键词： Convolutional neural networks

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biometric Authentification Comparison: Toward Secure human Recognition

Biometric Authentification Comparison: Toward Secure Human R...

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International conference on Computational Science and Computational Intelligence (CSCI)

作者： Mallouli, Fauna Khelifi, Nesrine Hellal, Aya Ferjani, Imen Chaabane, Nada Dakhlaoui, Mejda Chamakhi, Houda Imam Abdulrahman Bin Faisal Univ Deanship Preparatory Year & Supporting Studies Dammam 31441 Saudi Arabia Imam Abdulrahman Bin Faisal Univ Appl Coll Financial Sci Dept Dammam 31441 Saudi Arabia Imam Abdulrahman Bin Faisal Univ Appl Coll Dept Comp Sci Dammam 31441 Saudi Arabia Imam Abdulrahman Bin Faisal Univ Dept Quantitat Methods Coll Business Adm Dammam 31441 Saudi Arabia

ISBN: (纸本)9798350361513;9798350372304

Since the pandemic's announcement, covid-19 has hit almost all businesses. Regular office jobs and commercial meetings are changed and adopt the fully online new technology tools. To avoid coworkers' interaction with each other in all departments and face-to-face contact in person, all sectors of industry and all vital domains adopt the fully digital approach. The extreme need for new technology based on information systems leads users to adopt secure tools to face the pandemic. to protect the integrity and confidentiality of data. biometric Technologies are rapidly becoming the ideal solution to solve lots of problems including human identification. biometrics are declared to offer the security and accuracy of human being identification and authentication. However, each biometric check involves a series of processes that are interspersed with choices and contingencies on several levels. Additionally, there are underlying normative presumptions about human anatomy that have extremely severe effects on how biometric systems work. Several Recent research analyses have clarified gendered and racist prejudices as the cause of biometric system failures. The production, usage, and problematization of bodily differences during the research and design stages of biometric systems, as well as during their use, are highlighted by a more nuanced understanding of how biometrics and bodily differences intersect. Yet, every biometric check consists of a process with many intermediate steps, introducing contingency and choice on many levels. In addition, there are underlying normative assumptions regarding human bodies that affect the functioning of biometric systems in highly problematic ways. In recent social science studies, the failures of biometric systems have been interpreted as gendered and racialized biases. A more nuanced understanding of how biometrics and bodily differences intersect draws attention to how bodily differences are produced, used, and problematized during

关键词： biometric technology retina voice fingerprint Hand Geometry

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Features in Temporal and Frequency Domains of Eye-Blinking Signals for human identification 3

Features in Temporal and Frequency Domains of Eye-Blinking S...

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3rd International Joint conference on Information and Communication Engineering (JCICE)

作者： Yu, Kai Ye, Shenghua Zheng, Yangong Ningbo Univ Fac Elect Engn & Comp Sci Ningbo Peoples R China Zhejiang Fash Inst Technol Mech & Elect Engn Sch Ningbo Peoples R China

ISBN: (纸本)9798350388732;9798350388725

biometric verification has been recognized as an effective method for human identification. Compared to conventional biometrics, bioelectrical signals provide high security for personal recognition. Electrooculogram (EOG) signals of eye blinking have been shown to contain the unique characteristics of individuals. Previous studies have focused on features extracted from the shape of eye-blinking signals. This study compares the performance of features in the temporal and/or frequency domains extracted from eye blinking for human identification. The EOG of the eye blinking was collected using an electroencephalograph with a single electrode. In the feature extraction, the characteristics of eye blinking waveform in time delineation are extracted, at the same time, Fourier and wavelet transforms are applied for extracting features in frequency and time-frequency domains, respectively. The three types of features were computed using different classifiers. The best rate of 93.75% was achieved. By comparing their performance, the informative characteristics of personal identification are the varying frequencies of eye blinking over time. This study paves the way for building customized models for bioelectrical signal-based human identification.

关键词： biometric authentication classification electrooculogram wavelet transform

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35th British HCI conference Towards a human-Centred Digital Society, HCI 2022

35th British HCI Conference Towards a Human-Centred Digital ...

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35th British HCI conference Towards a human-Centred Digital Society, HCI 2022

The proceedings contain 66 papers. The topics discussed include: tools and technology to support rich community heritage;using audio-mixing software to facilitate remote data collection of conversational interactions;bring something to the potluck: a system for inclusive and reciprocal online discussion;assessing task difficulty in software testing using biometric measures;shedding light on assessing dark patterns: introducing the system darkness scale (SDS);blended experience narratives;the impact of language on students’ emotional states in educational games: a comparative study;exploring the effectiveness of adaptation based on dyslexia type and reading skill level to support learners with dyslexia;designing a peer assessment for identification of students’ group work problems;understanding the needs of Arab learners with dyslexia for adaptive systems;and cyber eyes wide open: creative collaboration between artists, academics & cybersecurity practitioners.

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human identification by Dynamics of Changes in Brain Frequencies Using Artificial Neural Networks 25th

Human Identification by Dynamics of Changes in Brain Frequen...

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25th International conference on Speech and Computer (SPECOM)

作者： Wolf, Daniyar Turovsky, Yaroslav Meshcheryakov, Roman Iskhakova, Anastasia Russian Acad Sci VA Trapeznikov Inst Control Sci Moscow Russia

ISBN: (纸本)9783031483080;9783031483097

The article considers the problem of improving the methods of human identification using biometric features, in particular, the signals of an electroencephalogram. The authors present the results of scientific research into human identification by dynamics of frequency changes of the brain using the known architecture of artificial neural networks: AlexNet and Mobile Net 2. The basic hypothesis is formulated that the waves registered by sensors from head leads are unique for each person. The authors describe the preparation of experimental data on the basis of electroencephalogram signals received as a result of experiments on the formation of steady-state visual evoked potentials in a group of people with the subsequent creation of an applied database. The achievability of the set task was based on assessments of the relevance and representativeness of the obtained data. Frequency-time characteristics were taken as training datasets. Using deep machine learning technology, two classification models are obtained that allow identifying the identity of a person with a probability of 70%. The evaluation of the adequacy of the obtained classification models is carried out with PCA and t-SNE algorithms;the efficiency of their work is evaluated. The results of deep machine learning andmachine classification tasks were confirmed by assessments of the adequacy of the obtained models. As a result, the authors confirm the main hypothesis.

关键词： Electroencephalogram human-Machine Interaction Brain-Computer Interface biometric Personal Characteristic identification Automation of identification Processes Machine Learning Processing of Bioelectric Signals Data Analysis

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Feature Extraction of the human Ear Based on Enhanced Active Contour Method 2

Feature Extraction of the Human Ear Based on Enhanced Active...

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2nd International conference on Machine Learning and Data Engineering, ICMLDE 2023

作者： Hemamalini, V. Annapurani, K. Saha, Pinaki Batra, Kushal Chatterjee, Jaydeep SRM Institute of Science and Technology Department of Networking and Communications Chennai603203 India

human ear detection is one of the key features in biometric-based identification and person authentication. Detecting ears for position, dimension, and texture is tedious due to the height and movement of humans. The biometric systems based on ear detection are defaced by the height of humans and specific positions. For this purpose, a multi-feature and multi-contour-based assessment for precise detection is required. To address this problem, this article introduces an Enhanced Active Contour Method (EACM) for improving ear detection precision. The proposed method extracts the textural features for differentiating the antitragus and helix of the human ear. The extracted features are fused using a fuzzy threshold for the maximum possibilities of the ear structures. In verifying the maximum possibilities, the helix and antitragus contours are fused by identifying similar features. The features from the existing dataset are used for verifying the similarity;the similarity succeeding contours are used for detecting human ears. The proposed method maximizes accuracy and precision by 12.99% and 12.79% and reduces extraction time by 11.34%. © 2024 Elsevier B.V.. All rights reserved.

关键词： Feature extraction

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Study and Implementation on FPGA of human Recognition System via Iris Based on Deep Learning

Study and Implementation on FPGA of Human Recognition System...

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2023 International conference on Decision Aid Sciences and Applications, DASA 2023

作者： Maroua, Rekrouk Rihame, Nbri Fatima Zohra, Allam Latifa, Hamami Ecole Natioanle Polytechnique Dept. of Electronics Algiers Algeria

ISBN: (纸本)9798350342055

Due to the distinct and consistent traits found in the human iris, iris recognition systems have garnered significant attention for biometric identification and authentication purposes. The project presents an extensive investigation and practical implementation of an iris recognition system on an Field Programmable Gate Arrays (FPGA). The primary objective is to develop an efficient and dependable biometric authentication solution. The study introduces an innovative software-based approach that improves system performance and addresses critical challenges associated with iris recognition technology. The valuable insights obtained from this project are expected to drive future advancements in iris recognition technology, while also contributing to the ongoing development of more secure and trustworthy biometric authentication systems. © 2023 IEEE.

关键词： biometrics

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Ethical Considerations and Legal Frameworks for biometric Surveillance Systems: The Intersection of AI, Soft biometrics, and human Surveillance

Ethical Considerations and Legal Frameworks for Biometric Su...

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International conference on. Cryptology and Network Security with Machine Learning, ICCNSML 2023

作者： Punia, Meenakshi Choudhary, Arjun Agarwal, Sonu Shukla, Varun Centre for Economic Crimes and Counter-Corruption Studies Sardar Patel University of Police Security and Criminal Justice Jodhpur India Centre for Cyber Security Sardar Patel University of Police Security and Criminal Justice Jodhpur India Faculty of Law Manipal University Jaipur India Kanpur India

ISBN: (纸本)9789819706402

biometric surveillance systems, combined with artificial intelligence (AI) and soft biometrics, have revolutionized the identification, and tracking of individuals based on their unique physical and behavioral characteristics. However, the integration of these technologies raises important ethical considerations and necessitates robust legal frameworks. This paper explores the amalgamation of AI, soft biometrics, and human surveillance, focusing on the ethical implications and legal regulations governing their use. The paper provides an overview of biometric surveillance systems, explaining the components and types of biometric modalities employed, such as fingerprints, facial recognition, iris recognition, voice recognition, and gait analysis. It highlights the applications and benefits of biometric surveillance systems in various domains, including law enforcement, access control, and health care. Ethical considerations in the deployment of biometric surveillance systems are thoroughly examined, including privacy concerns, informed consent, discrimination, and bias, and ensuring accountability and responsible use of technology. The significance of robust legal frameworks is emphasized, and an analysis of existing laws and regulations in different jurisdictions, including the UN, USA, Europe, France, Japan, India, Australia, and New Zealand, is provided. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： biometrics

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Convolutional Neural Network Based Implementation of human Ear Recognition System

Convolutional Neural Network Based Implementation of Human E...

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2023 International conference on Sustainable Emerging Innovations in Engineering and technology, ICSEIET 2023

作者： Sharma, Nitin Gupta, Kriti Maurya, Princy Gorai, Deepesh Himanshu Nuhu, Goodluck E. Chandigarh University Department Ece Mohali India

ISBN: (纸本)9798350329186

human ear recognition is a biometric identification technique which uses unique features of an ear to identify an individual. With the help of ear recognition system, we can add an extra layer of security to the existing system of facial recognition or fingerprint recognition. The structure of the ear remains fairly the same and doesn't change much with aging as compared to face and it also doesn't need to be in close proximity as required in case of fingerprint recognition which makes it ideal tool for passive identification and verification. In this paper, for ear detection, we have used Convolutional Neural Network (CNN) algorithm which is particularly used for image recognition. It consists of three layers namely convolutional layer, pooling layer and fully-connected layer. We aim to improve the robustness and accuracy of the biometric identification technique using CNN. The technique has been applied on own created dataset. © 2023 IEEE.

关键词： Convolution

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human identification at a Distance: Challenges, Methods and Results on the Competition HID 2024 18

Human Identification at a Distance: Challenges, Methods and ...

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18th IEEE International Joint conference on biometrics, IJCB 2024

作者： Yu, Shiqi Wu, Weiming Hu, Jiacong Wang, Zepeng Wang, Jingjie Zhang, Meng Wang, Runsheng Ni, Yunfei Huang, Yongzhen Wang, Liang Ahad, Md Atiqur Rahman Southern University of Science and Technology China South China University of Technology China Shanghai Jiao Tong University China Beijing Jiaotong University China Fujitsu Limited Japan Huazhong University of Science and Technology China Beijing Normal University China Watrix Technology Limited Co. Ltd China Chinese Academy of Sciences Institute of Automation China University of East London United Kingdom

ISBN: (纸本)9798350364132

human identification at a distance (HID) faces challenges due to the difficulty of acquiring traditional biometric modalities like face and fingerprints. Gait recognition offers a viable solution since it can be captured at a distance. To advance the algorithm development and provide fair evaluations, the International Competition on human identification at a Distance (HID) has been held annually since 2020, with HID 2024 marking the fifth edition. Despite increased difficulty, participants demonstrated remarkable capabilities, surpassing previous accuracy levels. This paper, co-authored by competition organizers and top participants, provides a comprehensive summary of HID 2024, including an overview of the competition, and insights into the methods employed by the top teams. Specifically, inspired by the achievements of the 5 competitions of HID, we also provide the insights for the future directions on gait recognition. © 2024 IEEE.

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