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CASIA-Iris-Africa:A Large-scale African Iris Image Database

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Machine Intelligence Research 2024年第2期21卷 383-399页

作者： Jawad Muhammad Yunlong Wang Junxing Hu Kunbo Zhang Zhenan Sun School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Center for Research on Intelligent Perception and Computing National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing 100190China

Iris biometrics is a phenotypic biometric trait that has proven to be agnostic to human natural physiological changes.Research on iris biometrics has progressed tremendously,partly due to publicly available iris databases.Various databases have been available to researchers that address pressing iris biometric challenges such as constraint,mobile,multispectral,synthetics,long-distance,contact lenses,liveness detection,etc.However,these databases mostly contain subjects of Caucasian and Asian docents with very few Africans.Despite many investigative studies on racial bias in face biometrics,very few studies on iris biometrics have been published,mainly due to the lack of racially diverse large-scale databases containing sufficient iris samples of Africans in the public domain.Furthermore,most of these databases contain a relatively small number of subjects and labelled images.This paper proposes a large-scale African database named Chinese Academy of Sciences Institute of Automation(CASIA)-Iris-Africa that can be used as a complementary database for the iris recognition community to mediate the effect of racial biases on Africans.The database contains 28717 images of 1023 African subjects(2046 iris classes)with age,gender,and ethnicity attributes that can be useful in demographically sensitive studies of Africans.Sets of specific application protocols are incorporated with the database to ensure the database’s variability and scalability.Performance results of some open-source state-of-the-art(SOTA)algorithms on the database are presented,which will serve as baseline performances.The relatively poor performances of the baseline algorithms on the proposed database despite better performance on other databases prove that racial biases exist in these iris recognition algorithms.

关键词： African iris recognition racial bias iris image database biometrics iris recognition

Extraction of intersecting palm-vein and palmprint features for cancellable identity verification

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CAAI Transactions on Intelligence Technology 2024年第1期9卷 69-86页

作者： Jaekwon Lee Beom-Seok Oh Kar-Ann Toh School of Electrical and Electronic Engineering Yonsei UniversitySeoulRepublic of Korea Department of Applied Artificial Intelligence Seoul National University of Science and TechnologySeoulRepublic of Korea

A novel method based on the cross-modality intersecting features of the palm-vein and the palmprint is proposed for identity verification.Capitalising on the unique geometrical relationship between the two biometric modalities,the cross-modality intersecting points provides a stable set of features for identity verification.To facilitate flexibility in template changes,a template transformation is proposed.While maintaining non-invertibility,the template transformation allows transformation sizes beyond that offered by the con-ventional means.Extensive experiments using three public palm databases are conducted to verify the effectiveness the proposed system for identity recognition.

关键词： biometrics hand vein recognition hand-based biometrics multi-biometrics palmprint recognition

A Hand Features Based Fusion Recognition Network with Enhancing Multi-Modal Correlation

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Computer Modeling in Engineering & Sciences 2024年第7期140卷 537-555页

作者： Wei Wu Yuan Zhang Yunpeng Li Chuanyang Li YanHao School of Information Engineering Shenyang UniversityShenyang110044China

Fusing hand-based features in multi-modal biometric recognition enhances anti-spoofing capabilities.Additionally,it leverages inter-modal correlation to enhance recognition performance.Concurrently,the robustness and recognition performance of the system can be enhanced through judiciously leveraging the correlation among multimodal features.Nevertheless,two issues persist in multi-modal feature fusion recognition:Firstly,the enhancement of recognition performance in fusion recognition has not comprehensively considered the inter-modality correlations among distinct modalities.Secondly,during modal fusion,improper weight selection diminishes the salience of crucial modal features,thereby diminishing the overall recognition performance.To address these two issues,we introduce an enhanced DenseNet multimodal recognition network founded on feature-level fusion.The information from the three modalities is fused akin to RGB,and the input network augments the correlation between modes through channel correlation.Within the enhanced DenseNet network,the Efficient Channel Attention Network(ECA-Net)dynamically adjusts the weight of each channel to amplify the salience of crucial information in each modal feature.Depthwise separable convolution markedly reduces the training parameters and further enhances the feature correlation.Experimental evaluations were conducted on four multimodal databases,comprising six unimodal databases,including multispectral palmprint and palm vein databases from the Chinese Academy of Sciences.The Equal Error Rates(EER)values were 0.0149%,0.0150%,0.0099%,and 0.0050%,correspondingly.In comparison to other network methods for palmprint,palm vein,and finger vein fusion recognition,this approach substantially enhances recognition performance,rendering it suitable for high-security environments with practical applicability.The experiments in this article utilized amodest sample database comprising 200 individuals.The subsequent phase involves preparing for the extension of the method to larger data

关键词： biometrics multi-modal correlation deep learning feature-level fusion

Enhance the Performance of Directional Feature-based Palmprint Recognition by Directional Response Stability Measurement

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Machine Intelligence Research 2024年第3期21卷 597-614页

作者： Haitao Wang Wei Jia School of Computer Science and Information Engineering Hefei University of TechnologyHefei230009China

Palmprint recognition is an emerging biometrics technology that has attracted increasing attention in recent years. Many palmprint recognition methods have been proposed, including traditional methods and deep learning-based methods. Among the traditional methods, the methods based on directional features are mainstream because they have high recognition rates and are robust to illumination changes and small noises. However, to date, in these methods, the stability of the palmprint directional response has not been deeply studied. In this paper, we analyse the problem of directional response instability in palmprint recognition methods based on directional feature. We then propose a novel palmprint directional response stability measurement (DRSM) to judge the stability of the directional feature of each pixel. After filtering the palmprint image with the filter bank, we design DRSM according to the relationship between the maximum response value and other response values for each pixel. Using DRSM, we can judge those pixels with unstable directional response and use a specially designed encoding mode related to a specific method. We insert the DRSM mechanism into seven classical methods based on directional feature, and conduct many experiments on six public palmprint databases. The experimental results show that the DRSM mechanism can effectively improve the performance of these methods. In the field of palmprint recognition, this work is the first in-depth study on the stability of the palmprint directional response, so this paper has strong reference value for research on palmprint recognition methods based on directional features.

关键词： biometrics palmprint recognition directional response stability directional coding-based methods directional feature.

Dynamic Signature Verification Using Pattern Recognition

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Journal of Software Engineering and Applications 2024年第5期17卷 214-227页

作者： Emmanuel Nwabueze Ekwonwune Duroha Austin Ekekwe Chinyere Iheakachi Ubochi Henry Chinedu Oleribe Department of Computer Science Imo State University Owerri Nigeria Department of Computer Science Gregory University Abia Nigeria ICT Department Alvan Ikoku University of Education Owerri Nigeria

Dynamic signature is a biometric modality that recognizes an individual’s anatomic and behavioural characteristics when signing their name. The rampant case of signature falsification (Identity Theft) was the key motivating factor for embarking on this study. This study was necessitated by the damages and dangers posed by signature forgery coupled with the intractable nature of the problem. The aim and objectives of this study is to design a proactive and responsive system that could compare two signature samples and detect the correct signature against the forged one. Dynamic Signature verification is an important biometric technique that aims to detect whether a given signature is genuine or forged. In this research work, Convolutional Neural Networks (CNNsor ConvNet) which is a class of deep, feed forward artificial neural networks that has successfully been applied to analysing visual imagery was used to train the model. The signature images are stored in a file directory structure which the Keras Python library can work with. Then the CNN was implemented in python using the Keras with the TensorFlow backend to learn the patterns associated with the signature. The result showed that for the same CNNs-based network experimental result of average accuracy, the larger the training dataset, the higher the test accuracy. However, when the training dataset are insufficient, better results can be obtained. The paper concluded that by training datasets using CNNs network, 98% accuracy in the result was recorded, in the experimental part, the model achieved a high degree of accuracy in the classification of the biometric parameters used.

关键词： Verification Security biometrics Signature Authentication Model Pattern Recognition Dynamic

OTB-morph:One-time biometrics via Morphing

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Machine Intelligence Research 2023年第6期20卷 855-871页

作者： Mahdi Ghafourian Julian Fierrez Ruben Vera-Rodriguez Aythami Morales Ignacio Serna biometrics and Data Pattern Analytics Laboratory(BiDA Lab) Universidad Autonoma de MadridMadrid28049Spain

Cancelable biometrics are a group of techniques to transform the input biometric to an irreversible feature intentionally using a transformation function and usually a key in order to provide security and privacy in biometric recognition systems.This transformation is repeatable enabling subsequent biometric comparisons.This paper introduces a new idea to be exploited as a transformation function for cancelable biometrics aimed at protecting templates against iterative optimization attacks.Our proposed scheme is based on time-varying keys(random biometrics in our case)and morphing transformations.An experimental implementation of the proposed scheme is given for face biometrics.The results confirm that the proposed approach is able to withstand leakage attacks while improving the recognition performance.

关键词： biometrics face recognition template protection morphing security

ECG biometrics via Enhanced Correlation and Semantic-rich Embedding

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Machine Intelligence Research 2023年第5期20卷 697-706页

作者： Kui-Kui Wang Gong-Ping Yang Lu Yang Yu-Wen Huang Yi-Long Yin School of Software Shandong UniversityJinan 250101China School of Computer Heze UniversityHeze 274015China School of Computer Science and Technology Shandong Jianzhu UniversityJinan 250101China

Electrocardiogram(ECG)biometric recognition has gained considerable attention,and various methods have been proposed to facilitate its development.However,one limitation is that the diversity of ECG signals affects the recognition performance.To address this issue,in this paper,we propose a novel ECG biometrics framework based on enhanced correlation and semantic-rich embedding.Firstly,we construct an enhanced correlation between the base feature and latent representation by using only one projection.Secondly,to fully exploit the semantic information,we take both the label and pairwise similarity into consideration to reduce the influence of ECG sample diversity.Furthermore,to solve the objective function,we propose an effective and efficient algorithm for optimization.Finally,extensive experiments are conducted on two benchmark datasets,and the experimental results show the effectiveness of our framework.

关键词： biometrics matrix factorization electrocardiogram(ECG) semantic information enhanced correlation

biometrics as a Matrix: The Short Distance between Crime and Security Systems, Prompting an Artificial Intelligence to Invent Electronic biometrics ID!

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International Journal of Intelligence Science 2022年第1期12卷 1-8页

作者： Ahmed Laarfi Brevard Public School Brevard FL USA

This paper reviews the essential biometrics and develops a way to combine them with the Computer and User Information, giving us an Electronic biometrics ID. This way, distributed databases contain imperative data fro... 详细信息

This paper reviews the essential biometrics and develops a way to combine them with the Computer and User Information, giving us an Electronic Biometrics ID. This way, distributed databases contain imperative data from much helpful information that supports more security. We reviewed examples of what these databases would look like, which any responsible party could design to be global. As will be mentioned later, we obtain common international databases whose data are modified according to factors such as the owner of the device, the location of the device, and so on. This is very useful for tracking, and it combines biometrics with data set to give us a comprehensive electronic identification.

关键词： biometrics Security Privacy Distributed System Databases

Multimodal Biometric Fusion Algorithm Based on Ranking Partition Collision Theory

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Machine Intelligence Research 2023年第6期20卷 884-896页

作者： Zhuorong Li Yunqi Tang School of Criminal Investigation People’s Public Security University of ChinaBeijing100038China

Score-based multimodal biometric fusion has been shown to be successful in addressing the problem of unimodal techniques’vulnerability to attack and poor performance in low-quality data.However,difficulties still exist in how to unify the meaning of heterogeneous scores more effectively.Aside from the matching scores themselves,the importance of the ranking information they include has been undervalued in previous studies.This study concentrates on matching scores and their ranking information and suggests the ranking partition collision(RPC)theory from the standpoint of the worth of scores.To meet both forensic and judicial needs,this paper proposes a method that employs a neural network to fuse biometrics at the score level.In addition,this paper constructs a virtual homologous dataset and conducts experiments on it.Experimental results demonstrate that the proposed method achieves an accuracy of 100%in both mAP and Rank1.To show the efficiency of the proposed method in practical applications,this work carries out more experiments utilizing real-world data.The results show that the proposed approach maintains a Rank1 accuracy of 99.2%on the million-scale database.It offers a novel approach to fusion at the score level.

关键词： Image processing convolutional neural network multimodal biometrics fusion