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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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A Data Perspective on Ethical Challenges in Voice biometrics Research

IEEE TRANSACTIONS ON BIOMETRICS, BEHAVIOR, AND IDENTITY SCIE...

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IEEE TRANSACTIONS ON biometricS, BEHAVIOR, AND IDENTITY SCIENCE 2025年第1期7卷 118-131页

作者： Leschanowsky, Anna Rusti, Casandra Quinlan, Carolyn Pnacek(ova), Michaela Gorce, Lauriane Hutiri, Wiebke Fraunhofer Inst Integrierte Schaltungen IIS D-91058 Erlangen Germany Univ Southern Calif Los Angeles CA 90089 USA Univ Toronto Toronto ON M5S 1A1 Canada York Univ Toronto ON M3J 1P3 Canada Mines Paris PSL F-75272 Paris France Delft Univ Technol Delft Netherlands

Speaker recognition technology, deployed in sectors like banking, education, recruitment, immigration, law enforcement, and healthcare, relies heavily on biometric data. However, the ethical implications and biases inherent in the datasets driving this technology have not been fully explored. Through a longitudinal study of close to 700 papers published at the ISCA Interspeech conference in the years 2012 to 2021, we investigate how dataset use has evolved alongside the widespread adoption of deep neural networks. Our study identifies the most commonly used datasets in the field and examines their usage patterns. The analysis reveals significant shifts in data practices since the advent of deep learning: a small number of datasets dominate speaker recognition training and evaluation, and the majority of studies evaluate their systems on a single dataset. For four key datasets-Switchboard, Mixer, VoxCeleb, and ASVspoof-we conduct a detailed analysis of metadata and collection methods to assess ethical concerns and privacy risks. Our study highlights numerous challenges related to sampling bias, re-identification, consent, disclosure of sensitive information and security risks in speaker recognition datasets, and emphasizes the need for more representative, fair, and privacy-aware data collection in this domain.

关键词： Speaker recognition Training Face recognition NIST Hidden Markov models Switches Speech recognition Ethical aspects privacy biometrics (access control) speaker recognition human voice data transparency data handling

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Bimodal biometric human Recognition System Using Deep Learning Algorithm

Bimodal Biometric Human Recognition System Using Deep Learni...

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Machine Learning and Autonomous Systems (ICMLAS), International conference on

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

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

关键词： Deep learning Accuracy Machine learning algorithms Reviews Noise Convolutional neural networks Security Protection Computer crime Iris recognition

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A Real-Time Vital Sign Monitoring and human identification Using WiFi Signals

A Real-Time Vital Sign Monitoring and Human Identification U...

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IEEE conference on Wireless Communications and Networking

作者： Baichuan Yao Gang Xie Jiangzhou Li Ruikang Wang Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

WiFi sensing represents an emerging technology that enables human sensing in a low-cost, non-invasive manner. Respiration, as a vital sign, can be employed not only for the assessment of physiological conditions but also for human identification. This paper proposes a highly accurate real-time vital sign monitoring algorithm and a high-precision person identification algorithm. The real-time monitoring algorithm utilizes WiFi device antenna diversity and Maximum Ratio Combining Discrete Wavelet Transform (MRC-DWT) to recover respiration signals, combining with the Autocorrelation Function (ACF) for accurate instantaneous respiration estimation. The identification algorithm leverages biometric information from respiration waveforms and the Multi-dimensional Dynamic Time Warping (MD-DTW) algorithm with modern classifiers for high-precision recognition. This paper validates the efficacy of the proposed algorithms by leveraging the WiFi Respiration Track (WiRes-Track) System. Experimental results demonstrate that the system is capable of discriminating among multiple users, attaining an average recognition accuracy of 91% for six users and a respiration rate accuracy of 96.8%

关键词： biometrics Accuracy Heuristic algorithms Spatial diversity Diversity reception Real-time systems Classification algorithms Sensors Wireless fidelity Monitoring

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biometric technology for human identification vii

Biometric Technology for Human Identification VII

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biometric technology for human identification vii

ISBN: (纸本)9780819481313

The proceedings contain 31 papers. The topics discussed include: sketch-to-photo matching: a feature-based approach;a novel thermal face recognition approach using face pattern words;superquadric representation of 3D faces: toward reducing time complexity of recognition;face recognition for uncontrolled environments;face recognition motivated by human approach;mobile, contactless, single-shot, fingerprint capture system;tools for quality control of fingerprint databases;visual cryptography for face privacy;analytical template protection performance and maximum key size given a Gaussian-modeled biometric source;cryptographically secure biometrics;a hierarchical fingerprint alignment method and its application to fuzzy vault;a central-limit theorem for a single false match rate;multi-order analysis framework for comprehensive biometric performance evaluation;a definitional framework for the human/biometric sensor interaction model;and significance test in operational ROC analysis.

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A Definitional Framework for the human-biometric Sensor Interaction Model

A Definitional Framework for the Human-Biometric Sensor Inte...

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conference on biometric technology for human identification vii

作者： Elliott, Stephen J. Kukula, Eric P. Purdue Univ Biometr Stand Performance & Assurance BSPA Lab W Lafayette IN 47907 USA

ISBN: (纸本)9780819481313

Existing definitions for biometric testing and evaluation do not fully explain errors in a biometric system. This paper provides a definitional framework for the human biometric-Sensor Interaction (HBSI) model. This paper proposes six new definitions based around two classifications of presentations, erroneous and correct. The new terms are: defective interaction (DI), concealed interaction (CI), false interaction (FI), failure to detect (FTD), failure to extract (FTX), and successfully acquired samples (SAS). As with all definitions, the new terms require a modification to the general biometric model developed by Mansfield and Wayman [1].

关键词： testing and evaluation performance terminology failure to acquire data collection acquisition usability general biometric model

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Robust human identification Using ECG: Eigenpulse Revisited

Robust Human Identification Using ECG: Eigenpulse Revisited

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conference on biometric technology for human identification vii

作者： Jang, Daniel Wendelken, Suzanne Irvine, John M. Scitor Corp Cambridge MA 02139 USA

ISBN: (纸本)9780819481313

biometrics, such as fingerprint, iris scan, and face recognition, offer methods for identifying individuals based on a unique physiological measurement. Recent studies indicate that a person's electrocardiogram (ECG) may also provide a unique biometric signature. Several methods for processing ECG data have appeared in the literature and most approaches rest on an initial detection and segmentation of the heartbeats. Various sources of noise, such as sensor noise, poor sensor placement, or muscle movements, can degrade the ECG signal and introduce errors into the heartbeat segmentation. This paper presents a screening technique for assessing the quality of each segmented heartbeat. Using this technique, a higher quality signal can be extracted to support the identification task. We demonstrate the benefits of this quality screening using a principal component technique known as eigenpulse. The analysis demonstrated the improvement in performance attributable to the quality screening.

关键词： biometrics identification electrocardiogram ECG EKG Principal Components

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Gait Curves for human Recognition, Backpack Detection and Silhouette Correction in a Nighttime Environment

Gait Curves for Human Recognition, Backpack Detection and Si...

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conference on biometric technology for human identification vii

作者： DeCann, Brian Ross, Arun W Virginia Univ Morgantown WV 26506 USA

ISBN: (纸本)9780819481313

The need for an automated surveillance system is pronounced at night when the capability of the human eye to detect anomalies is reduced. While there have been significant efforts in the classification of individuals using human metrology and gait, the majority of research assumes a day-time environment. The aim of this study is to move beyond traditional image acquisition modalities and explore the issues of object detection and human identification at night. To address these issues, a spatiotemporal gait curve that captures the shape dynamics of a moving human silhouette is employed. Initially proposed by Wang et al.,(1) this representation of the gait is expanded to incorporate modules for individual classification, backpack detection, and silhouette restoration. Evaluation of these algorithms is conducted on the CASIA Night Gait Database, which includes 10 video sequences for each of 153 unique subjects. The video sequences were captured using a low resolution thermal camera. Matching performance of the proposed algorithms is evaluated using a nearest neighbor classifier. The outcome of this work is an efficient algorithm for backpack detection and human identification, and a basis for further study in silhouette enhancement.

关键词： gait recognition object detection silhouette extraction gait curves nighttime biometrics

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ECG Based biometric human identification Using Chaotic Encryption 2

ECG Based Biometric Human Identification Using Chaotic Encry...

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2nd International conference on Electrical Engineering and Information and Communication technology (ICEEICT)

作者： Jahiruzzaman, Md Hossain, A. B. M. Aowlad Khulna Univ Engn & Technol Dept Elect & Commun Engn Khulna 9203 Bangladesh

ISBN: (纸本)9781467366762

biometric identification provides a reliable performance on recognition of identity for different security and authentication management problems. With the development of technology, the privacy and security is the main concern as technology can be falsified. In recent decades biometric traits are the trusted choice to the researchers and there are strong documentations on biometric traits like human electrocardiogram which is the time changing electrical impulse signal passing through heart is able to use for identity subject identification. In this work, an analytic and serviceable analysis of the ECG for human identification is presented. A new approach based on chaotic encryption (CE) of continuous wavelet transformed (CWT) ECG signal has been proposed. The CWT used here to obtain similarity pattern of each individual and CE of the CWT decomposed signal used for subject identification. The proposed approach has been tested using 11 different subjects ECG signal available at MIT-BIH arrhythmia database. The obtained results show that the identification rate is 96.82%. The findings of this study can be helpful to develop effective biometric human identification system.

关键词： biometric identification electrocardiogram continious wavelet transform chaotic encryptition

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Enhancing eye movement based biometric identification method by using voting classifiers

Enhancing eye movement based biometric identification method...

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conference on biometric technology for human identification II

作者： Kasprowski, P Ober, J Silesian Tech Univ Inst Informat PL-44100 Gliwice Poland

ISBN: (纸本)0819457647

Eye movements contain a lot of information about human being. The way the eyes are moving is very complicated and eye movement patterns has been subject of studies for over 100 years. However, surprisingly, eye movement based identification is a quite new idea presented for the first time during the biometrics' 2003 conference in London [17]. The method has several significant advantages: compiles behavioral and physiological properties of human body, it is difficult to forge and it is affordable - with a number of ready-to-use eye registering devices (so called eye trackers). The paper introduces the methodology and presents results of the first eye movement based authorization tests.

关键词： biometrics eye movements human identification eye-trackers

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