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Dual-domain and Multiscale Fusion Deep Neural Network for PPG biometric recognition

Machine Intelligence Research 2023年第5期20卷 707-715页

作者： Chun-Ying Liu Gong-Ping Yang Yu-Wen Huang Fu-Xian Huang School of computer Heze UniversityHeze 274015China School of Software Shandong UniversityJinan 250101China

Photoplethysmography(PPG)biometrics have received considerable attention.Although deep learning has achieved good performance for PPG biometrics,several challenges remain open:1)How to effectively extract the feature fusion representation from time and frequency PPG signals.2)How to effectively capture a series of PPG signal transition information.3)How to extract timevarying information from one-dimensional time-frequency sequential data.To address these challenges,we propose a dual-domain and multiscale fusion deep neural network(DMFDNN)for PPG biometric recognition.The DMFDNN is mainly composed of a two-branch deep learning framework for PPG biometrics,which can learn the time-varying and multiscale discriminative features from the time and frequency domains.Meanwhile,we design a multiscale extraction module to capture transition information,which consists of multiple convolution layers with different receptive fields for capturing multiscale transition information.In addition,the dual-domain attention module is proposed to strengthen the domain of greater contributions from time-domain and frequency-domain data for PPG biometrics.Experiments on the four datasets demonstrate that DMFDNN outperforms the state-of-the-art methods for PPG biometrics.

关键词： Photoplethysmography(PPG)signal biometric recognition multiple scale deep neural network dual-domain attention.

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A Comprehensive Survey for Privacy-Preserving biometrics: Recent Approaches, Challenges, and Future Directions

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Computers, Materials & Continua 2024年第2期78卷 2087-2110页

作者： Shahriar Md Arman Tao Yang Shahadat Shahed Alanoud AlMazroa Afraa Attiah Linda Mohaisen School of Computer Science and Technology Zhejiang Gongshang UniversityHangzhou310018China School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou310018China College of Computer and Information Sciences Princess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia

The rapid growth of smart technologies and services has intensified the challenges surrounding identity authenti-cation techniques.biometric credentials are increasingly being used for verification due to their advantages over traditional methods,making it crucial to safeguard the privacy of people’s biometric data in various scenarios.This paper offers an in-depth exploration for privacy-preserving techniques and potential threats to biometric systems.It proposes a noble and thorough taxonomy survey for privacy-preserving techniques,as well as a systematic framework for categorizing the field’s existing literature.We review the state-of-the-art methods and address their advantages and limitations in the context of various biometric modalities,such as face,fingerprint,and eye detection.The survey encompasses various categories of privacy-preserving mechanisms and examines the trade-offs between security,privacy,and recognition performance,as well as the issues and future research directions.It aims to provide researchers,professionals,and decision-makers with a thorough understanding of the existing privacy-preserving solutions in biometric recognition systems and serves as the foundation of the development of more secure and privacy-preserving biometric technologies.

关键词： biometric modalities biometric recognition biometric security privacy-preserving security threats

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Unconstrained Hand Dorsal Veins Image Database and recognition System

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Computers, Materials & Continua 2022年第12期73卷 5063-5073页

作者： Mustafa M.Al Rifaee Mohammad M.Abdallah Mosa I.Salah Ayman M.Abdalla Faculty of Science and Information Technology Al-Zaytoonah University of JordanAmman11733Jordan Faculty of Architecture and Design Al-Zaytoonah University of JordanAmman11733Jordan

Hand veins can be used effectively in biometric recognition since they are internal organs that,in contrast to fingerprints,are robust under external environment effects such as dirt and paper cuts.Moreover,they form a complex rich shape that is unique,even in identical twins,and allows a high degree of freedom.However,most currently employed hand-based biometric systems rely on hand-touch devices to capture images with the desired quality.Since the start of the COVID-19 pandemic,most handbased biometric systems have become undesirable due to their possible impact on the spread of the pandemic.Consequently,new contactless hand-based biometric recognition systems and databases are desired to keep up with the rising hygiene awareness.One contribution of this research is the creation of a database for hand dorsal veins images obtained contact-free with a variation in capturing distance and rotation angle.This database consists of 1548 images collected from 86 participants whose ages ranged from 19 to 84 years.For the other research contribution,a novel geometrical feature extraction method has been developed based on the Curvelet Transform.This method is useful for extracting robust rotation invariance features from vein images.The database attributes and the veins recognition results are analyzed to demonstrate their efficacy.

关键词： biometric recognition contactless hand biometrics veins recognition Curvelet transform image segmentation feature extraction

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biometric feature extraction using local fractal auto-correlation

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Chinese Physics B 2014年第9期23卷 335-340页

作者：陈熙张家树 School of Information Engineering and Automation Kunming University of Science and Technology Key Lab of Signal & Information Processing of Sichuan Province Southwest Jiaotong University

Image texture feature extraction is a classical means for biometric recognition. To extract effective texture feature for matching, we utilize local fractal auto-correlation to construct an effective image texture descriptor. Three main steps are involved in the proposed scheme：（i） using two-dimensional Gabor filter to extract the texture features of biometric images; （ii） calculating the local fractal dimension of Gabor feature under different orientations and scales using fractal auto-correlation algorithm; and （iii） linking the local fractal dimension of Gabor feature under different orientations and scales into a big vector for matching. Experiments and analyses show our proposed scheme is an efficient biometric feature extraction approach.

关键词： fractal auto-correlation fractal dimension Gabor filter biometric recognition

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Iris recognition Technique

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Journal of Electronic Science and Technology of China 2006年第3期4卷 219-224页

作者：解梅 School of Electronic Engineering University of Electronic Science and Technology of China Chengdu 610054 China

The demand on security is increasing greatly in these years and biometric recognition gradually becomes a hot field of research. Iris recognition is a new branch of biometric recognition, which is regarded as the most stable, safe and accurate biometric recognition method. In these years, much progress in this field has been made by scholars and experts of different countries. In this paper, some successful iris recognition methods are listed and their performance are compared. Furthermore, the existing problems and challenges are discussed.

关键词： biometric recognition iris localization normalization feature vectorextraction and match

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