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Li M, Gong Y, Zheng Z. Finger Vein Identification Based on Large Kernel Convolution and Attention Mechanism. SENSORS (BASEL, SWITZERLAND) 2024; 24:1132. [PMID: 38400290 PMCID: PMC10892868 DOI: 10.3390/s24041132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Revised: 02/04/2024] [Accepted: 02/06/2024] [Indexed: 02/25/2024]
Abstract
FV (finger vein) identification is a biometric identification technology that extracts the features of FV images for identity authentication. To address the limitations of CNN-based FV identification, particularly the challenge of small receptive fields and difficulty in capturing long-range dependencies, an FV identification method named Let-Net (large kernel and attention mechanism network) was introduced, which combines local and global information. Firstly, Let-Net employs large kernels to capture a broader spectrum of spatial contextual information, utilizing deep convolution in conjunction with residual connections to curtail the volume of model parameters. Subsequently, an integrated attention mechanism is applied to augment information flow within the channel and spatial dimensions, effectively modeling global information for the extraction of crucial FV features. The experimental results on nine public datasets show that Let-Net has excellent identification performance, and the EER and accuracy rate on the FV_USM dataset can reach 0.04% and 99.77%. The parameter number and FLOPs of Let-Net are only 0.89M and 0.25G, which means that the time cost of training and reasoning of the model is low, and it is easier to deploy and integrate into various applications.
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Greco M, Eldridge M, Banks E, Halámková L, Halámek J. Metabolite monitoring concept for the biometric identification of individuals from the skin surface. Analyst 2024; 149:350-356. [PMID: 38018892 DOI: 10.1039/d3an01605f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2023]
Abstract
This study aims at proof of concept that constant monitoring of the concentrations of metabolites in three individuals' sweat over time can differentiate one from another at any given time, providing investigators and analysts with increased ability and means to individualize this bountiful biological sample. A technique was developed to collect and extract authentic sweat samples from three female volunteers for the analysis of lactate, urea, and L-alanine levels. These samples were collected 21 times over a 40-day period and quantified using a series of bioaffinity-based enzymatic assays with UV-vis spectrophotometric detection. Sweat samples were simultaneously dried, derivatized, and analyzed by a GC-MS technique for comparison. Both UV-vis and GC-MS analysis methods provided a statistically significant MANOVA result, demonstrating that the sum of the three metabolites could differentiate each individual at any given day of the time interval. Expanding upon previous studies, this experiment aims to establish a method of metabolite monitoring as opposed to single-point analyses for application to biometric identification from the skin surface.
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Ning E, Wang Y, Wang C, Zhang H, Ning X. Enhancement, integration, expansion: Activating representation of detailed features for occluded person re-identification. Neural Netw 2024; 169:532-541. [PMID: 37948971 DOI: 10.1016/j.neunet.2023.11.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 10/17/2023] [Accepted: 11/02/2023] [Indexed: 11/12/2023]
Abstract
A proposed method, Enhancement, integration, and Expansion, aims to activate the representation of detailed features for occluded person re-identification. Region and context are two important and complementary features, and integrating them in an occluded environment can effectively improve the robustness of the model. Firstly, a self-enhancement module is designed. Based on the constructed multi-stream architecture, rich and meaningful feature interference is introduced in the feature extraction stage to enhance the model's ability to perceive noise. Next, a collaborative integration module similar to cascading cross-attention is proposed. By studying the intrinsic interaction patterns of regional and contextual features, it adaptively fuses features across streams and enhances the diverse and complete representation of internal information. The module is not only robust to complex occlusions, but also mitigates the feature interference problem due to similar appearances or scenes. Finally, a matching expansion module that enhances feature discriminability and completeness is proposed. Providing more stable and accurate features for recognition. Compared with state-of-the-art methods on two occluded and holistic datasets, the proposed method is proved to be advanced and the effectiveness of the module is proved by extensive ablation studies.
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Ibrahim MEA, Abbas Q, Daadaa Y, Ahmed AES. A Novel PPG-Based Biometric Authentication System Using a Hybrid CVT-ConvMixer Architecture with Dense and Self-Attention Layers. SENSORS (BASEL, SWITZERLAND) 2023; 24:15. [PMID: 38202878 PMCID: PMC10781036 DOI: 10.3390/s24010015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Revised: 12/11/2023] [Accepted: 12/17/2023] [Indexed: 01/12/2024]
Abstract
Biometric authentication is a widely used method for verifying individuals' identities using photoplethysmography (PPG) cardiac signals. The PPG signal is a non-invasive optical technique that measures the heart rate, which can vary from person to person. However, these signals can also be changed due to factors like stress, physical activity, illness, or medication. Ensuring the system can accurately identify and authenticate the user despite these variations is a significant challenge. To address these issues, the PPG signals were preprocessed and transformed into a 2-D image that visually represents the time-varying frequency content of multiple PPG signals from the same human using the scalogram technique. Afterward, the features fusion approach is developed by combining features from the hybrid convolution vision transformer (CVT) and convolutional mixer (ConvMixer), known as the CVT-ConvMixer classifier, and employing attention mechanisms for the classification of human identity. This hybrid model has the potential to provide more accurate and reliable authentication results in real-world scenarios. The sensitivity (SE), specificity (SP), F1-score, and area under the receiver operating curve (AUC) metrics are utilized to assess the model's performance in accurately distinguishing genuine individuals. The results of extensive experiments on the three PPG datasets were calculated, and the proposed method achieved ACCs of 95%, SEs of 97%, SPs of 95%, and an AUC of 0.96, which indicate the effectiveness of the CVT-ConvMixer system. These results suggest that the proposed method performs well in accurately classifying or identifying patterns within the PPG signals to perform continuous human authentication.
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Jing X, He L, Song Z, Wang S. Audio-Visual Fusion Based on Interactive Attention for Person Verification. SENSORS (BASEL, SWITZERLAND) 2023; 23:9845. [PMID: 38139689 PMCID: PMC10747811 DOI: 10.3390/s23249845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 12/10/2023] [Accepted: 12/12/2023] [Indexed: 12/24/2023]
Abstract
With the rapid development of multimedia technology, personnel verification systems have become increasingly important in the security field and identity verification. However, unimodal verification systems have performance bottlenecks in complex scenarios, thus triggering the need for multimodal feature fusion methods. The main problem with audio-visual multimodal feature fusion is how to effectively integrate information from different modalities to improve the accuracy and robustness of the system for individual identity. In this paper, we focus on how to improve multimodal person verification systems and how to combine audio and visual features. In this study, we use pretrained models to extract the embeddings from each modality and then perform fusion model experiments based on these embeddings. The baseline approach in this paper involves taking the fusion feature and passing it through a fully connected (FC) layer. Building upon this baseline, we propose three fusion models based on attentional mechanisms: attention, gated, and inter-attention. These fusion models are trained on the VoxCeleb1 development set and tested on the evaluation sets of the VoxCeleb1, NIST SRE19, and CNC-AV datasets. On the VoxCeleb1 dataset, the best system performance achieved in this study was an equal error rate (EER) of 0.23% and a detection cost function (minDCF) of 0.011. On the evaluation set of NIST SRE19, the EER was 2.60% and the minDCF was 0.283. On the evaluation set of the CNC-AV set, the EER was 11.30% and the minDCF was 0.443. These experimental results strongly demonstrate that the proposed fusion method can significantly improve the performance of multimodal character verification systems.
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Regan ES, Bradshaw BT, Bruhn AM, Melvin W, Sikdar S. Populational Variations of Cheiloscopy Patterns: A cross-sectional observation pilot study. JOURNAL OF DENTAL HYGIENE : JDH 2023; 97:196-204. [PMID: 37816610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 09/01/2023] [Indexed: 10/12/2023]
Abstract
Purpose Lip prints are unique and have potential for use as a human identifier. The purpose of this study was to observe possible cheiloscopy differences of individuals with and without parafunctional oral habits such as smoking, vaping, playing a wind instrument or using an asthma inhaler.Methods This IRB approved blinded cross-sectional observation pilot study collected lip prints from sixty-six individuals, three of which were excluded. Participants cleansed their lips, then lipstick was applied to the vermillion zones of the upper and lower lips. Adhesive tape was applied to the lips and prints were transferred to white bond paper for viewing purposes. Each set of included lip prints was divided into quadrants and dichotomized into a group of those with an oral parafunctional habit or with no such habits. Each quadrant sample was then manually analyzed and classed according to the gold standard Suzuki and Tsuchihashi system.Results A total of 252 dichotomized lip print quadrants (with habits n=76, 30.2%, and without habits n=176, 69.8%) were analyzed. Type II patterns were the most common for examined quadrant samples; however, no statistically significant differences (Pearson's chi-squared test, p=0.366) were observed between pattern classifications of samples with and without parafunctional oral habits.Conclusion There is no statistically significant difference of lip print patterns between individuals with and without parafunctional oral habits. Further research on populational variations is needed for cheiloscopy to aid in human identifications.
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Mitchell ARJ, Ahlert D, Brown C, Birge M, Gibbs A. Electrocardiogram-based biometrics for user identification - Using your heartbeat as a digital key. J Electrocardiol 2023; 80:1-6. [PMID: 37058746 DOI: 10.1016/j.jelectrocard.2023.04.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Revised: 02/22/2023] [Accepted: 04/04/2023] [Indexed: 04/16/2023]
Abstract
External biometrics such as thumbprint and facial recognition have become standard tools for securing our digital devices and protecting our data. These systems, however, are potentially prone to copying and cybercrime access. Researchers have therefore explored internal biometrics, such as the electrical patterns within an electrocardiogram (ECG). The heart's electrical signals carry sufficient distinctiveness to allow the ECG to be used as an internal biometric for user authentication and identification. Using the ECG in this way has many potential advantages and limitations. This article reviews the history of ECG biometrics and explores some of the technical and security considerations. It also explores current and future uses of the ECG as an internal biometric.
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Fu T, Pradhan A, He J, He C, Jiang N. Comparison of Wrist and Forearm EMG for Multi-day Biometric Authentication. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38082655 DOI: 10.1109/embc40787.2023.10340339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Recently, electromyography (EMG) has been established as a promising new biometric trait that provides a unique dual mode security: biometrics and knowledge. For authentication that is used daily and long-term by general consumers, the wrist is a suitable location, which could be easily integrated into the existing form of smartwatches and fitness trackers. However, current EMG-based biometrics still follow the historical path of powered prosthetics research, where EMG signals were usually recorded from forearm positions. Moreover, the robustness of EMG processing algorithms across multiple days is still an open problem that needs to be addressed before for long-term reliable use. This study intends to investigate the difference in authentication performance between wrist and forearm EMG signals, in a within-day and two cross-day analyses. Our open dataset (GRABMyo dataset) was used to examine this difference, which contains forearm and wrist EMG data collected from 43 participants over three different days with long separation (Days 1, 8, and 29). The results showed wrist EMG signals led to at least comparable with forearm EMG signals in within-day Equal-error rate (EER). In cross-day analysis, the EER of the wrist EMG signals was higher than that of forearm signals. In general, the low median EER (<0.1) of wrist EMG in cumulative cross-day analysis demonstrates the promise of using wrist EMG signals for authentication in long-term applications.
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Chu HY, Lin TY, Lee SH, Chiu JK, Nien CP, Wu SC. A Scalable ECG Identification System Based on Locality-Sensitive Hashing. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083079 DOI: 10.1109/embc40787.2023.10341130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Electrocardiograms (ECGs) have the inherent property of being intrinsic and dynamic and are shown to be unique among individuals, making them promising as a biometric trait. Although many ECG biometric recognition approaches have demonstrated accurate recognition results in small enrollment sets, they can suffer from performance degradation when many subjects are enrolled. This study proposes an ECG biometric identification system based on locality-sensitive hashing (LSH) that can accommodate a large number of registrants while maintaining satisfactory identification accuracy. By incorporating the concept of LSH, the identity of an unknown subject can be recognized without performing vector comparisons for all registered subjects. Moreover, a kernel density estimator-based method is used to exclude unregistered subjects. The ECGs of 285 subjects from the PTB dataset were used to evaluate the proposed scheme's performance. Experimental results demonstrated an IR and EER of 99% and 4%, respectively, when Nen/Nid = 15/3.
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Seok CL, Song YD, An BS, Lee EC. Photoplethysmogram Biometric Authentication Using a 1D Siamese Network. SENSORS (BASEL, SWITZERLAND) 2023; 23:4634. [PMID: 37430548 PMCID: PMC10221126 DOI: 10.3390/s23104634] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 05/08/2023] [Accepted: 05/09/2023] [Indexed: 07/12/2023]
Abstract
In the head-mounted display environment for experiencing metaverse or virtual reality, conventional input devices cannot be used, so a new type of nonintrusive and continuous biometric authentication technology is required. Since the wrist wearable device is equipped with a photoplethysmogram sensor, it is very suitable for use for nonintrusive and continuous biometric authentication purposes. In this study, we propose a one-dimensional Siamese network biometric identification model using a photoplethysmogram. To maintain the unique characteristics of each person and reduce noise in preprocessing, we adopted a multicycle averaging method without using a bandpass or low-pass filter. In addition, to verify the effectiveness of the multicycle averaging method, the number of cycles was changed and the results were compared. Genuine and impostor data were used to verify the biometric identification. We used the one-dimensional Siamese network to verify the similarity between the classes and found that the method with five overlapping cycles was the most effective. Tests were conducted on the overlapping data of five single-cycle signals and excellent identification results were observed, with an AUC score of 0.988 and an accuracy of 0.9723. Thus, the proposed biometric identification model is time-efficient and shows excellent security performance, even in devices with limited computational capabilities, such as wearable devices. Consequently, our proposed method has the following advantages compared with previous works. First, the effect of noise reduction and information preservation through multicycle averaging was experimentally verified by varying the number of photoplethysmogram cycles. Second, by analyzing authentication performance through genuine and impostor matching analysis based on a one-dimensional Siamese network, the accuracy that is not affected by the number of enrolled subjects was derived.
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Okello E, Ayieko P, Kwena Z, Nanyonjo G, Bahemuka U, Price M, Bukusi E, Hashim R, Nakamanya S, Okech B, Kuteesa M, Oketch B, Ssetaala A, Ruzagira E, Kidega W, Fast P, Kibengo F, Grosskurth H, Seeley J, Kapiga S. Acceptability and applicability of biometric iris scanning for the identification and follow up of highly mobile research participants living in fishing communities along the shores of Lake Victoria in Kenya, Tanzania, and Uganda. Int J Med Inform 2023; 172:105018. [PMID: 36774907 DOI: 10.1016/j.ijmedinf.2023.105018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 01/31/2023] [Accepted: 02/03/2023] [Indexed: 02/12/2023]
Abstract
BACKGROUND Recruitment and retention of participants in research studies conducted in fishing communities remain a challenge because of population mobility. Reliable and acceptable methods for identifying and tracking participants taking part in HIV prevention and treatment research are needed. The study aims to assess the acceptability, and technical feasibility of iris scans as a biometric identification method for research participants in fishing communities. METHODS This was a cross-sectional study conducted in eight fishing communities in Kenya, Tanzania, and Uganda, with follow-up after one month in a randomly selected subset of participants. All consenting participants had their iris scanned and then responded to the survey. RESULTS 1,199 participants were recruited. The median age was 33 [Interquartile range (IQR) 24-42] years; 56% were women. The overall acceptability of iris scanning was 99%, and the success rate was 98%. Eighty one percent (n = 949) had a successful scan on first attempt, 116 (10%) on second and 113 (9%) after more than two attempts. A month later, 30% (n = 341) of participants were followed up. The acceptability of repeat iris scanning was 99% (n = 340). All participants who accepted repeat iris scanning had successful scans, with 307 (90%) scans succeeding on first attempt; 25 (7%) on second attempt, and 8 (2%) after several attempts. The main reason for refusing iris scanning was fear of possible side effects of the scan on the eyes or body. CONCLUSION The acceptability and applicability of biometric iris scan as a technique for unique identification of research participants is high in fishing communities. However, successful use of the iris scanning technology in research will require education regarding the safety of the procedure.
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Syed MA, Ou Y, Li T, Jiang G. Lightweight Multimodal Domain Generic Person Reidentification Metric for Person-Following Robots. SENSORS (BASEL, SWITZERLAND) 2023; 23:813. [PMID: 36679613 PMCID: PMC9866428 DOI: 10.3390/s23020813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 12/19/2022] [Accepted: 12/28/2022] [Indexed: 06/17/2023]
Abstract
Recently, person-following robots have been increasingly used in many real-world applications, and they require robust and accurate person identification for tracking. Recent works proposed to use re-identification metrics for identification of the target person; however, these metrics suffer due to poor generalization, and due to impostors in nonlinear multi-modal world. This work learns a domain generic person re-identification to resolve real-world challenges and to identify the target person undergoing appearance changes when moving across different indoor and outdoor environments or domains. Our generic metric takes advantage of novel attention mechanism to learn deep cross-representations to address pose, viewpoint, and illumination variations, as well as jointly tackling impostors and style variations the target person randomly undergoes in various indoor and outdoor domains; thus, our generic metric attains higher recognition accuracy of target person identification in complex multi-modal open-set world, and attains 80.73% and 64.44% Rank-1 identification in multi-modal close-set PRID and VIPeR domains, respectively.
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Jacobson ZE. Face Off: Overcoming the Fifth Amendment Conflict Between Cybersecurity and Self-Incrimination. JOURNAL OF LAW AND HEALTH 2023; 36:185-202. [PMID: 37585552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 08/18/2023]
Abstract
The Founders included the privilege against self-incrimination in the Constitution to protect individual privacy and ensure a fair judicial process. Courts have failed U.S. citizens by neglecting to protect them from compelled unlocking of biometrically encrypted devices. This inaction has created a loophole that contradicts the framework of the privilege against self-incrimination. To correct this mistake courts should reconsider the trend they have set for the Constitution and the Fifth Amendment and consider adopting a forward-thinking cybersecurity lens to conclude that biometric authentication is testimonial. Courts should consider that biometric encryption is akin to a compelled password entry for the purposes of the foregone conclusion doctrine. The foregone conclusion doctrine should be applied in limited circumstances with a specific and high burden of proof so that the "jealous protection of the privilege against self-incriminating testimony" can be preserved. Allowing law enforcement such easy access to smart devices narrows Fifth Amendment protections and the expansive foregone conclusion exception is contrary to both principles of cybersecurity and the spirit of the Fifth Amendment. Courts should move to remediate this at once. These liberties and values can only be guaranteed by courts that are willing to take on cases with issues revolving around biometric encryption, the Fifth Amendment, and the foregone conclusion doctrine.
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Sulavko A. Biometric-Based Key Generation and User Authentication Using Acoustic Characteristics of the Outer Ear and a Network of Correlation Neurons. SENSORS (BASEL, SWITZERLAND) 2022; 22:9551. [PMID: 36502251 PMCID: PMC9736167 DOI: 10.3390/s22239551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Revised: 11/13/2022] [Accepted: 11/25/2022] [Indexed: 06/17/2023]
Abstract
Trustworthy AI applications such as biometric authentication must be implemented in a secure manner so that a malefactor is not able to take advantage of the knowledge and use it to make decisions. The goal of the present work is to increase the reliability of biometric-based key generation, which is used for remote authentication with the protection of biometric templates. Ear canal echograms were used as biometric images. Multilayer convolutional neural networks that belong to the autoencoder type were used to extract features from the echograms. A new class of neurons (correlation neurons) that analyzes correlations between features instead of feature values is proposed. A neuro-extractor model was developed to associate a feature vector with a cryptographic key or user password. An open data set of ear canal echograms to test the performance of the proposed model was used. The following indicators were achieved: EER = 0.0238 (FRR = 0.093, FAR < 0.001), with a key length of 8192 bits. The proposed model is superior to known analogues in terms of key length and probability of erroneous decisions. The ear canal parameters are hidden from direct observation and photography. This fact creates additional difficulties for the synthesis of adversarial examples.
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He Q, Feng Z, Wang X, Wu Y, Yang J. A Smart Pen Based on Triboelectric Effects for Handwriting Pattern Tracking and Biometric Identification. ACS APPLIED MATERIALS & INTERFACES 2022; 14:49295-49302. [PMID: 36255736 DOI: 10.1021/acsami.2c13714] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
The rapid development of artificial intelligence places high demands on human-machine interfaces. Various types of huma-machine interfaces have been implemented, including smart keyboards, electronic skins, and wearable motion sensors. Handwriting behavior has a high degree of interaction freedom, and handwriting characteristics offer high-security standards for human-machine systems. Herein, we propose a portable smart pen integrated with triboelectric displacement vector sensors to trace handwriting trajectories for human-machine interactions and biometric identification. A triboelectric pressure sensor array is evenly distributed along the pen case to sense displacement vectors, and an additional triboelectric sensor is placed on top of the pen to detect vertical force. By leveraging the resin pen refill as a tribopositive material and a nanowired polyethylene tribonegative layer attached to a Cu electrode, triboelectric signals are generated during the writing/moving process. The calculation and analysis of the sensor signals enable the recognition of handwritten patterns, including Latin letters and Arabic numerals. Moreover, the smart pen can be used to authenticate users based on their unique handwriting patterns, which can help take human-machine interfaces and cyber security to the next level. Furthermore, a custom smart pen operation mode that enables the control of a slide presentation demonstrates the smart pen's potential for various human-machine interface applications.
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Bedari A, Wang S, Yang W. A Secure Online Fingerprint Authentication System for Industrial IoT Devices over 5G Networks. SENSORS (BASEL, SWITZERLAND) 2022; 22:7609. [PMID: 36236704 PMCID: PMC9572055 DOI: 10.3390/s22197609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 09/27/2022] [Accepted: 10/03/2022] [Indexed: 06/16/2023]
Abstract
The development of 5G networks has rapidly increased the use of Industrial Internet of Things (IIoT) devices for control, monitoring, and processing purposes. Biometric-based user authentication can prevent unauthorized access to IIoT devices, thereby safeguarding data security during production. However, most biometric authentication systems in the IIoT have no template protection, thus risking raw biometric data stored as templates in central databases or IIoT devices. Moreover, traditional biometric authentication faces slow, limited database holding capacity and data transmission problems. To address these issues, in this paper we propose a secure online fingerprint authentication system for IIoT devices over 5G networks. The core of the proposed system is the design of a cancelable fingerprint template, which protects original minutia features and provides privacy and security guarantee for both entity users and the message content transmitted between IIoT devices and the cloud server via 5G networks.Compared with state-of-the-art methods, the proposed authentication system shows competitive performance on six public fingerprint databases, while saving computational costs and achieving fast online matching.
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Alkasimi A, Shepard T, Wagner S, Pancrazio S, Pham AV, Gardner C, Funsten B. Dual-Biometric Human Identification Using Radar Deep Transfer Learning. SENSORS 2022; 22:s22155782. [PMID: 35957338 PMCID: PMC9371011 DOI: 10.3390/s22155782] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 07/29/2022] [Accepted: 07/29/2022] [Indexed: 02/04/2023]
Abstract
Accurate human identification using radar has a variety of potential applications, such as surveillance, access control and security checkpoints. Nevertheless, radar-based human identification has been limited to a few motion-based biometrics that are solely reliant on micro-Doppler signatures. This paper proposes for the first time the use of combined radar-based heart sound and gait signals as biometrics for human identification. The proposed methodology starts by converting the extracted biometric signatures collected from 18 subjects to images, and then an image augmentation technique is applied and the deep transfer learning is used to classify each subject. A validation accuracy of 58.7% and 96% is reported for the heart sound and gait biometrics, respectively. Next, the identification results of the two biometrics are combined using the joint probability mass function (PMF) method to report a 98% identification accuracy. To the best of our knowledge, this is the highest reported in the literature to date. Lastly, the trained networks are tested in an actual scenario while being used in an office access control platform to identify different human subjects. We report an accuracy of 76.25%.
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Huo DM, Mo WW, Zhao FM, Zhou ZH, DU M, Zheng JL, Ma KJ. Individual Identification in Facial Appearance Biometrics Based on Macroscopical Comparison. FA YI XUE ZA ZHI 2022; 38:308-313. [PMID: 36221818 DOI: 10.12116/j.issn.1004-5619.2020.200909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Individual identification is one of the research hotspots in the practice of forensic science, and the judgment is usually built on the comparison of the unique biological characteristics of the individual, such as fingerprints, iris and DNA. With the dramatic increase in the number of cases related to video image investigations, there is an increasing need for the technology to identify individuals based on the macroscopic comparison of facial appearance biometrics. At present, with the introduction of computer three-dimensional (3D) modeling and 3D superimposition comparison technology, considerable progress has been made in individual identification methods based on macroscopic comparison of facial appearance biometrics. This paper reviews individual facial appearance biometric methods based on macroscopical comparison, comprehensively analyzes the advantages and limitations of different methods, and puts forward recommendations and prospects for subsequent research.
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Kasprowski P, Borowska Z, Harezlak K. Biometric Identification Based on Keystroke Dynamics. SENSORS (BASEL, SWITZERLAND) 2022; 22:3158. [PMID: 35590848 PMCID: PMC9105156 DOI: 10.3390/s22093158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Revised: 04/14/2022] [Accepted: 04/18/2022] [Indexed: 06/15/2023]
Abstract
The purpose of the paper is to study how changes in neural network architecture and its hyperparameters affect the results of biometric identification based on keystroke dynamics. The publicly available dataset of keystrokes was used, and the models with different parameters were trained using this data. Various neural network layers-convolutional, recurrent, and dense-in different configurations were employed together with pooling and dropout layers. The results were compared with the state-of-the-art model using the same dataset. The results varied, with the best-achieved accuracy equal to 82% for the identification (1 of 20) task.
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20
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Sun L, Zhong Z, Qu Z, Xiong N. PerAE: An Effective Personalized AutoEncoder for ECG-based Biometric in Augmented Reality System. IEEE J Biomed Health Inform 2022; 26:2435-2446. [PMID: 35077376 DOI: 10.1109/jbhi.2022.3145999] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
With the development of the Augmented and Virtual Reality (AR/VR) technologies, massive biometric data are collected by different organizations. These data have great significance but also worsen the privacy risks. Electro-CardioGram (ECG)-based Identity Recognition (EIR) is a popular Biometric technology. An ECG record is an internal Biology feature of a person and has time continuity. Thus, compared with traditional Biometric methods like face recognition, EIR may be less vulnerable to attack. We propose an Autoencoder-based EIR system, called Personalized AutoEncoder (PerAE). PerAE maintains a small autoencoder model (called Attention-MemAE) for each registered user of a system. The Attention-MemAE enhances the autoencoder by using a memory module and two attention mechanisms. A users Attention-MemAE classifies the hearbeats of other users as anomalies. An Attention-MemAE can be updated when the distribution of the users ECG data is changed. By using personalized autoencoder, PerAE can improve the time efficiency and reduce the memory overhead. It improves the adaptability, scalability, and maintainability of EIR systems. Experiment results show that to train an Attention-MemAE with 90% identification accuracy for a user, we can just take five minutes to collect the users ECG data (around 500 heartbeat samples).
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21
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Sae-Bae N, Memon N. Distinguishability of keystroke dynamic template. PLoS One 2022; 17:e0261291. [PMID: 35061684 PMCID: PMC8782503 DOI: 10.1371/journal.pone.0261291] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 11/28/2021] [Indexed: 11/19/2022] Open
Abstract
When keystroke dynamics are used for authentication, users tend to get different levels of security due to differences in the quality of their templates. This paper addresses this issue by proposing a metric to quantify the quality of keystroke dynamic templates. That is, in behavioral biometric verification, the user’s templates are generally constructed using multiple enrolled samples to capture intra-user variation. This variation is then used to normalize the distance between a set of enrolled samples and a test sample. Then a normalized distance is compared against a predefined threshold value to derive a verification decision. As a result, the coverage area for accepted samples in the original space of vector representation is discrete. Therefore, users with the higher intra-user variation suffer higher false acceptance rates (FAR). This paper proposes a metric that can be used to reflect the verification performance of individual keystroke dynamic templates in terms of FAR. Specifically, the metric is derived from statistical information of user-specific feature variations, and it has a non-decreasing property when a new feature is added to a template. The experiments are performed based on two public keystroke dynamic datasets comprising of two main types of keystroke dynamics: constrained-text and free-text, namely the CMU keystroke dynamics dataset and the Web-Based Benchmark for keystroke dynamics dataset. Experimental results based on multiple classifiers demonstrate that the proposed metric can be a good indicator of the template’s false acceptance rate. Thus, it can be used to enhance the security of the user authentication system based on keystroke dynamics.
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22
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Sun Y, Leng L, Jin Z, Kim BG. Reinforced Palmprint Reconstruction Attacks in Biometric Systems. SENSORS 2022; 22:s22020591. [PMID: 35062552 PMCID: PMC8781289 DOI: 10.3390/s22020591] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 01/08/2022] [Accepted: 01/10/2022] [Indexed: 02/05/2023]
Abstract
Biometric signals can be acquired with different sensors and recognized in secure identity management systems. However, it is vulnerable to various attacks that compromise the security management in many applications, such as industrial IoT. In a real-world scenario, the target template stored in the database of a biometric system can possibly be leaked, and then used to reconstruct a fake image to fool the biometric system. As such, many reconstruction attacks have been proposed, yet unsatisfactory naturalness, poor visual quality or incompleteness remains as major limitations. Thus, two reinforced palmprint reconstruction attacks are proposed. Any palmprint image, which can be easily obtained, is used as the initial image, and the region of interest is iteratively modified with deep reinforcement strategies to reduce the matching distance. In the first attack, Modification Constraint within Neighborhood (MCwN) limits the modification extent and suppresses the reckless modification. In the second attack, Batch Member Selection (BMS) selects the significant pixels (SPs) to compose the batch, which are simultaneously modified to a slighter extent to reduce the matching number and the visual-quality degradation. The two reinforced attacks can satisfy all the requirements, which cannot be simultaneously satisfied by the existing attacks. The thorough experiments demonstrate that the two attacks have a highly successful attack rate for palmprint systems based on the most state-of-the-art coding-based methods.
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Zhang Z, Tran L, Liu F, Liu X. On Learning Disentangled Representations for Gait Recognition. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2022; 44:345-360. [PMID: 32750777 DOI: 10.1109/tpami.2020.2998790] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Gait, the walking pattern of individuals, is one of the important biometrics modalities. Most of the existing gait recognition methods take silhouettes or articulated body models as gait features. These methods suffer from degraded recognition performance when handling confounding variables, such as clothing, carrying and viewing angle. To remedy this issue, we propose a novel AutoEncoder framework, GaitNet, to explicitly disentangle appearance, canonical and pose features from RGB imagery. The LSTM integrates pose features over time as a dynamic gait feature while canonical features are averaged as a static gait feature. Both of them are utilized as classification features. In addition, we collect a Frontal-View Gait (FVG) dataset to focus on gait recognition from frontal-view walking, which is a challenging problem since it contains minimal gait cues compared to other views. FVG also includes other important variations, e.g., walking speed, carrying, and clothing. With extensive experiments on CASIA-B, USF, and FVG datasets, our method demonstrates superior performance to the SOTA quantitatively, the ability of feature disentanglement qualitatively, and promising computational efficiency. We further compare our GaitNet with state-of-the-art face recognition to demonstrate the advantages of gait biometrics identification under certain scenarios, e.g., long-distance/lower resolutions, cross viewing angles. Source code is available at http://cvlab.cse.msu.edu/project-gaitnet.html.
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Ghazarian A, Zheng J, El-Askary H, Chu H, Fu G, Rakovski C. Increased Risks of Re-identification For Patients Posed by Deep Learning-Based ECG Identification Algorithms. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:1969-1975. [PMID: 34891673 DOI: 10.1109/embc46164.2021.9630880] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
ECGs analysis is an important tool in cardiac diagnosis. ECG data also have the potential to be used as a biometric source that allows precise person identification similar to the widely used fingerprint and iris recognition techniques. However, this phenomenon also raises serious privacy concerns. In this study, we provide a complete, multi-step ECG identification algorithm using a private database of ECG recordings. We train and validate our AI model on approximately 40k patients which makes this study by far the largest research project in this field. Moreover, our best model attained an exceptionally high accuracy of 94.56%. In addition to discussing the general implications of the deployment of such systems related to privacy, for the first time, we also assess the accuracy of ECG-based identification for distinct heart condition groups (and combinations of such conditions) and the corresponding privacy implications. For instance, we discovered that in contrast to initial expectation that identification accuracy for healthy normal sinus rhythm should be the highest, the identification accuracy is higher for patients with sinus tachycardia or patients who are diagnosed with both ST changes and supraventricular tachycardia. This puts these patients at a higher risk of privacy issues due to re-identification. On the other hand, we observed that patients with premature ventricular contractions have an identification accuracy as low as 78.54%. The identification rate for patients with a pacemaker is 80.2%.Clinical relevance-While ECG as a biometric can be a potentially useful technology, it also raises serious concerns regarding the privacy of cardiac patients. Especially, the ECG Identification algorithms empowered by deep learning can increase the risk of re-identification.
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Chiu JK, Chang CS, Wu SC. ECG-based Biometric Recognition without QRS Segmentation: A Deep Learning-Based Approach. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:88-91. [PMID: 34891246 DOI: 10.1109/embc46164.2021.9630899] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Electrocardiogram (ECG)-based identification systems have been widely studied in the literature. Usually, an ECG trace needs to be segmented according to the detected R peaks to enable feature extraction from the ECGs of duration equal to nearly one cardiac cycle. Beat averaging should also be applied to reduce the influence of inter-beat variation on the extracted features and identification accuracy. Either detecting R peaks or collecting extra heartbeats for averaging will inevitably lead to a delay in the identification process. This paper proposes a deep learning-based ECG biometric identification scheme that allows identity recognition using a random ECG segment without needing R-peak detection and beat averaging. Moreover, the problem of being vulnerable to unregistered subjects in an identification system is also addressed. Experimental results demonstrated that an identification rate of 99.1% for an identification system having 235 enrollees with an equal error rate of 8.08% was achieved.
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