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Journal articles on the topic 'Multi-Biometric'

Author: Grafiati

Published: 7 June 2025

Last updated: 16 July 2025

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1

Artabaz, Saliha, Layth Sliman, Karima Benatchba, and Mouloud Koudil. "Optimized multi‐biometric enhancement analysis." IET Biometrics 10, no. 3 (2021): 326–41. http://dx.doi.org/10.1049/bme2.12026.

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Barni, Mauro, Giulia Droandi, Riccardo Lazzeretti, and Tommaso Pignata. "SEMBA: secure multi‐biometric authentication." IET Biometrics 8, no. 6 (2019): 411–21. http://dx.doi.org/10.1049/iet-bmt.2018.5138.

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Ashish, Dabas, Shalini Bhadola Ms., and Kirti Bhatia Ms. "Storage of Biometric Data in Database." International Journal of Trend in Scientific Research and Development 3, no. 3 (2019): 1001–4. https://doi.org/10.31142/ijtsrd23146.

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Storage of multi biometric information is required to encourage quick inquiry in expansive scale biometric frameworks. Past works tending to this issue in multi biometric databases concentrated on multi case ordering, fundamentally iris information. Scarcely any works tended to the ordering in multi modular databases, with fundamental competitor list combination arrangements restricted to joining face and unique mark information. Iris and unique finger impression are generally utilized in vast scale biometric frameworks where quick recovery is a critical issue. This work proposes joint multi b

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Omar, Bayan, Hamsa D. Majeed, Siti Zaiton Mohd Hashim, and Muzhir Al-Ani. "New Feature-level Algorithm for a Face-fingerprint Integral Multi-biometrics Identification System." UHD Journal of Science and Technology 6, no. 1 (2022): 12–20. http://dx.doi.org/10.21928/uhdjst.v6n1y2022.pp12-20.

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This article delves into the power of multi-biometric fusion for individual identification. a new feature-level algorithm is proposed that is the Dis-Eigen algorithm. Here, a feature-fusion framework is proposed for attaining better accuracy when identifying individuals for multiple biometrics. The framework, therefore, underpins the new multi-biometric system as it guides multi-biometric fusion applications at the feature phase for identifying individuals. In this regard, the Face-fingerprints of 20 individuals represented by 160 images were used in this framework . Experimental resultants of

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Assouma, Abdoul Kamal, Tahirou Djara, and Abdou-Aziz Sobabe. "Multi-Biometrics: Survey and Projection of a New Biometric System." International Journal of Engineering and Advanced Technology 12, no. 3 (2023): 80–87. http://dx.doi.org/10.35940/ijeat.c4008.0212323.

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Multi-biometric systems using feature-level fusion allow more accuracy and reliability in recognition performance than uni-biometric systems. But in practice, this type of fusion is difficult to implement especially when we are facing heterogeneous biometric modalities or incompatible features. The major challenge of feature fusion is to produce a representation of each modality with an excellent level of discrimination. Beyond pure biometric modalities, the use of metadata has proven to improve the performance of biometric systems. In view of these findings, our work focuses on multi-origin b

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Abdoul, Kamal Assouma, Djara Tahirou, and Sobabe Abdou-Aziz. "Multi-Biometrics: Survey and Projection of a New Biometric System." International Journal of Engineering and Advanced Technology (IJEAT) 12, no. 3 (2023): 80–87. https://doi.org/10.35940/ijeat.C4008.0212323.

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<strong>Abstract: </strong>Multi-biometric systems using feature-level fusion allow more accuracy and reliability in recognition performance than uni-biometric systems. But in practice, this type of fusion is difficult to implement especially when we are facing heterogeneous biometric modalities or incompatible features. The major challenge of feature fusion is to produce a representation of each modality with an excellent level of discrimination. Beyond pure biometric modalities, the use of metadata has proven to improve the performance of biometric systems. In view of these findings, our wor

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Patil, Sonali D., Roshani Raut, Rutvij H. Jhaveri, et al. "Robust Authentication System with Privacy Preservation of Biometrics." Security and Communication Networks 2022 (May 2, 2022): 1–14. http://dx.doi.org/10.1155/2022/7857975.

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IoT-based multi-biometric system is a blend of multiple biometric templates that can be used for user authentication/verification using sensors. The leakage of the biometric trait information may cause critical privacy and security issues. It is expected to protect the privacy details of individuals through the irreversibility, unlinkability, and renewability of multi-biometric templates used in the authentication system. This study presents a robust authentication system with secure multi-biometric template protection techniques based on discrete cosine transform feature transformation and La

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Gopika Sri M, Jayakarthika K, Karthiga G, and Dr. P. Umaeswari4. "Biometric Authentication: Advances in Multi-Modal Biometric Systems for Enhanced Security." International Research Journal on Advanced Engineering and Management (IRJAEM) 3, no. 03 (2025): 559–62. https://doi.org/10.47392/irjaem.2025.0089.

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Biometric authentication methods verify identity by using distinctive characteristics such as voice patterns, face features, or fingerprints. Despite depending on a single characteristic, single-modal systems may have problems such as poor accuracy or spoofing susceptibility. Poor illumination or damaged fingerprints, for instance, can impair performance. Multi-modal systems, which integrate two or more characteristics (such as facial and fingerprint identification), provide increased security and accuracy by lowering rejections or false matches. This paper's conclusion offers suggestions for

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Nwani, Emmanuel Chinweuba. "Intricacies of Secured Multi-Biometric System." TEXILA INTERNATIONAL JOURNAL OF ACADEMIC RESEARCH 4, no. 2 (2017): 237–43. http://dx.doi.org/10.21522/tijar.2014.04.02.art024.

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10

Lazarick, Richard T. "Multi-purpose biometric performance grading scheme." International Journal of Biometrics 5, no. 1 (2013): 99. http://dx.doi.org/10.1504/ijbm.2013.050735.

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11

Kolda, Lukas, Ondrej Krejcar, Ali Selamat, Kamil Kuca, and Oluwaseun Fadeyi. "Multi-Biometric System Based on Cutting-Edge Equipment for Experimental Contactless Verification." Sensors 19, no. 17 (2019): 3709. http://dx.doi.org/10.3390/s19173709.

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Biometric verification methods have gained significant popularity in recent times, which has brought about their extensive usage. In light of theoretical evidence surrounding the development of biometric verification, we proposed an experimental multi-biometric system for laboratory testing. First, the proposed system was designed such that it was able to identify and verify a user through the hand contour, and blood flow (blood stream) at the upper part of the hand. Next, we detailed the hard and software solutions for the system. A total of 40 subjects agreed to be a part of data generation

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Rachapalli, Devendra Reddy, and Hemantha Kumar Kalluri. "Texture Driven Hierarchical Fusion for Multi-Biometric Sys-tem." International Journal of Engineering & Technology 7, no. 4.24 (2018): 33. http://dx.doi.org/10.14419/ijet.v7i4.24.21766.

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This article presents hierarchical fusion models for multi-biometric systems with improved recognition rate. Isolated texture regions are used to encode spatial variations from the composite biometric image which is generated by signal level fusion scheme. In this paper, the prominent issues of the existing multi-biometric system, namely, fusion methodology, storage complexity, reliability and template security are discussed. Here wavelet decomposition driven multi-resolution approach is used to generate the composite images. Texture feature metrics are extracted from multi-level texture regio

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Sabharwal, Munish. "Multi-Modal Biometric Authentication and Secure Transaction Operation Framework for E-Banking." International Journal of Business Data Communications and Networking 13, no. 1 (2017): 102–16. http://dx.doi.org/10.4018/ijbdcn.2017010109.

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The rationale of the research work is to suggest a multi-modal biometric authentication and secure transaction operation framework for E-Banking. The literature survey identifies the various types of E-Banking Channels available as on-date, the various types of biometric technologies available as on-date as well the significant metrics affecting their performance while deploying them in various different e-banking channels. The performance analysis of various types of biometric technologies based on significant metrics for Biometrics Implementation further identifies the currently implementabl

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Saleem, Sehrish, Shahzad Ashraf, and Muhammad K Basit. "CMBA - A CANDID MULTI-PURPOSE BIOMETRIC APPROACH." ICTACT Journal on Image and Video Processing 11, no. 1 (2020): 2211–16. https://doi.org/10.21917/ijivp.2020.0317.

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All humans are born with unique physically identified body characteristics to other persons which remains unchanged throughout life. These characteristics are taken into account by the emerging technology to get recognized from person to person. The technology used by the traditional human identification system sometimes becomes inefficient when data or images received are not up to the acceptable quality mark or when a person has a face covered with mask-like during epidemic virus fistula. In order to overcome the human recognition challenges, a Candid Multi-purpose Biometric Approach (CMBA)

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Sai Satyanarayana Reddy, S., Harikrishna Bommala, G. R. Sakthidharan, and Nikolai Ivanovich Vatin. "Multi-Modal Biometric Recognition for Face and Iris using Gradient Neural Network (Gen-NN)." MATEC Web of Conferences 392 (2024): 01078. http://dx.doi.org/10.1051/matecconf/202439201078.

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In recent years, Biometric system are the one, which is widely used method for the recognition and identification of an individual that are highly demanded approach for its absolute security and accuracy which plays a vital roles in banking, commercials, business and other fields. Moreover this research is based on the multimodal biometrics which is recommended for its high recognition performances and it overcome the demerits of unimodal biometric approach. This research concentrate two multi-modal biometric traits such as face and iris, and propose Gradient Neural Network (Gen-NN) method to

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Karthi, G., and M. Ezhilarasan. "Multi biometric Template Protection using Hybrid Technique." International Journal of Engineering & Technology 7, no. 4 (2018): 2609. http://dx.doi.org/10.14419/ijet.v7i4.11485.

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Recently, multi-biometrics system has been the important identification system for providing authentication mechanism. In this pa-per, the multi-biometric recognition system uses multiple traits (face, iris and fingerprint) for authentication. The features are extracted from the traits and feature level fusion technique is applied to the individual features traits to form a fused feature. Protection of these biometrics features against various attacks points is an important concern for authentication process. One such attack is the modification of stored template, which largely affects the per

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17

Srivastava, Rohit. "Score-Level Multimodal Biometric Authentication of Humans Using Retina, Fingerprint, and Fingervein." International Journal of Applied Evolutionary Computation 11, no. 3 (2020): 20–30. http://dx.doi.org/10.4018/ijaec.2020070102.

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This paper characterizes a multi-modular framework for confirmation, dependent on the biometric combination of retina, finger vein, and unique mark acknowledgment. The authors have proposed feature extraction in retina acknowledgment model by utilizing SIFT and MINUTIA. Security is the fundamental idea in ATM (Automated Teller Machines) today. The use of multi-modular biometrics can be ATM. The work includes three biometric attributes of a client to be specific retina, unique mark, and finger veins. These are pre-prepared and joined (fused) together for score level combination approach. Retina

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18

Manvi Khatri and Ajay Sharma. "A Computational Approach for Score-Level Fusion Decision-Making of Multi-biometric Recognition System Using Ant Colony Optimisation." Journal of Advanced Research in Applied Sciences and Engineering Technology 36, no. 1 (2023): 147–58. http://dx.doi.org/10.37934/araset.36.1.147158.

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This study presents a novel deep learning approach for improving the performance of a multi-biometric recognition system using Ant Colony Optimisation (ACO) at the score level. The proposed method integrates three biometric characteristics, the face, palm, and iris, into a single recognition input. A deep learning model, convolutional neural networks (CNNs), is employed to extract discriminative features from each attribute. The ACO algorithm optimizes the score-level fusion procedure, in which recognition scores from the combined input are combined to make the final determination. The experim

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19

Memon, Qurban A. "Multi-Layered Multimodal Biometric Authentication for Smartphone Devices." International Journal of Interactive Mobile Technologies (iJIM) 14, no. 15 (2020): 222. http://dx.doi.org/10.3991/ijim.v14i15.15825.

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As technological advances in smartphone domain increase, so are the issues that pertain to security and privacy. In current literature, multimodal biometric approach is addressed at length for purpose of improving secured access into personal devices. Moreover, most of the financial institutions such as banks, etc. enforce two or three step access into their corporate data to enforce security. However, personal devices currently do not support similar applications or way of enforcing multilayered access to its different domains/regions of data. In this paper, a multilayered multimodal biometri

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Sneha, Kurhekar, and Upadhyay Harshvardhan. "MULTI-MODEL BIOMETRICS AUTHENTICATION FRAMEWORK." International Journal of Engineering Sciences & Research Technology 5, no. 2 (2016): 849–54. https://doi.org/10.5281/zenodo.46532.

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Authentication is the process to conform the truth of an attribute claimed by real entity. Biometric technology is widely useful for the process of authentication. Today, biometric is becoming a key aspect in a multitude of applications. So this paper proposed the applications of such a multimodal biometric authentication system. Proposed system establishes a real time authentication framework using multi-model biometrics which consists of the embedded system verify the signatures, fingerprint and key pattern to authenticate the user.  This is one of the most reliable, fast and cost effec

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Medjahed, Chahreddine, Abdellatif Rahmoun, Christophe Charrier, and Freha Mezzoudj. "A deep learning-based multimodal biometric system using score fusion." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 1 (2022): 65. http://dx.doi.org/10.11591/ijai.v11.i1.pp65-80.

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Recent trends in artificial intelligence tools-based biometrics have overwhelming attention to security matters. The hybrid approaches are motivated by the fact that they combine mutual strengths and they overcome their limitations. Such approaches are being applied to the fields of biomedical engineering. A biometric system uses behavioural or physiological characteristics to identify an individual. The fusion of two or more of these biometric unique characteristics contributes to improving the security and overcomes the drawbacks of unimodal biometric-based security systems. This work propos

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Chahreddine, Medjahed, Rahmoun Abdellatif, Charrier Christophe, and Mezzoudj Freha. "A deep learning-based multimodal biometric system using score fusion." International Journal of Artificial Intelligence (IJ-AI) 11, no. 1 (2022): 65–80. https://doi.org/10.11591/ijai.v11.i1.pp65-80.

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Recent trends in artificial intelligence tools-based biometrics have overwhelming attention to security matters. The hybrid approaches are motivated by the fact that they combine mutual strengths and they overcome their limitations. Such approaches are being applied to the fields of biomedical engineering. A biometric system uses behavioural or physiological characteristics to identify an individual. The fusion of two or more of these biometric unique characteristics contributes to improving the security and overcomes the drawbacks of unimodal biometric-based security systems. This work propos

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R, Sreemol, and Kavitha N. "A Review on Different Multi Biometric Cryptosystems." IJARCCE 6, no. 3 (2017): 1000–1001. http://dx.doi.org/10.17148/ijarcce.2017.63232.

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M, Gobi, and Sridevi R. "Multi-Biometric Authentication through Hybrid Cryptographic System." INTERNATIONAL JOURNAL OF COMPUTING ALGORITHM 4, no. 1 (2015): 19–21. http://dx.doi.org/10.20894/ijcoa.101.004.001.004.

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J. Al-Kubaisy, Wigdan, and Muzhir S. Al-Ani. "Personal Identification based on Multi Biometric Traits." Journal of University of Anbar for Pure Science 11, no. 1 (2017): 68–75. http://dx.doi.org/10.37652/juaps.2017.141533.

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Mercy, Benson-Emenike, and Nwachukwu E.O. "An Improved Intelligent Multi Biometric Authentication System." Communications on Applied Electronics 3, no. 4 (2015): 27–38. http://dx.doi.org/10.5120/cae2015651930.

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Drozdowski, Pawel, Christian Rathgeb, Benedikt-Alexander Mokros, and Christoph Busch. "Multi-Biometric Identification With Cascading Database Filtering." IEEE Transactions on Biometrics, Behavior, and Identity Science 2, no. 3 (2020): 210–22. http://dx.doi.org/10.1109/tbiom.2020.2977215.

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Sudhakar, Tanuja, and Marina Gavrilova. "Deep Learning for Multi-instance Biometric Privacy." ACM Transactions on Management Information Systems 12, no. 1 (2020): 1–23. http://dx.doi.org/10.1145/3389683.

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Dhameja, Sandeep. "Multi-Characteristic Biometric Systems: Who Are You?" Information Systems Security 14, no. 1 (2005): 46–54. http://dx.doi.org/10.1201/1086/45098.14.1.20050301/87271.7.

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Boquete, Luciano, José Manuel Rodríguez Ascariz, Joaquín Cantos, et al. "A portable wireless biometric multi-channel system." Measurement 45, no. 6 (2012): 1587–98. http://dx.doi.org/10.1016/j.measurement.2012.02.018.

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Danese, G., M. Giachero, F. Leporati, and N. Nazzicari. "An embedded multi-core biometric identification system." Microprocessors and Microsystems 35, no. 5 (2011): 510–21. http://dx.doi.org/10.1016/j.micpro.2011.03.003.

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Ashiba, H. I., and F. E. Abd El-Samie. "Implementation face based cancelable multi-biometric system." Multimedia Tools and Applications 79, no. 41-42 (2020): 30813–38. http://dx.doi.org/10.1007/s11042-020-09529-7.

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Nafea, Ohoud, Sanaa Ghouzali, Wadood Abdul, and Emad-ul-Haq Qazi. "Hybrid Multi-Biometric Template Protection Using Watermarking." Computer Journal 59, no. 9 (2015): 1392–407. http://dx.doi.org/10.1093/comjnl/bxv107.

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Abed alkareem hussain Ayash, hawraa, and hawraa hassan abbas. "a survey on multi-biometric fusion approaches." Kerbala Journal for Engineering Sciences 3, no. 2 (2023): 79–100. https://doi.org/10.63463/kjes1074.

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The goal of biometrics is to reliably and robustly identify people based on their unique personal characteristics, primarily for security and authentication needs, but also to identify and track the users of more intelligent applications. Fingerprints, iris, palm print, face and voices are frequently used modalities, but there are numerous more potential biometrics, such as stride, ear image, retina, DNA, and even behavior. As an automatic way to identify persons depend on just one (single modal biometrics) or a mix of (multi-modal biometrics). A fusion of two or more photos can be utilized to

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Prof.Ankur, Kumar Aggarwal, and Himanshu Bansal Mr. "An Innovation in Multi-model Biometric Techniques." Journal of Optical Communication Electronics 4, no. 3 (2018): 11–26. https://doi.org/10.5281/zenodo.1459100.

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<em>In this paper gives an overview of working, challenges of face recognition and fingerprint recognition system. Multi-model recognition system is better as compare to single model because if one model is intruded another template is used. The paper presents the details of 3D face model acquisition and its recognition techniques. Challenges faced in face recognition system leads to 3D face recognition system. Different level of threats to the biometric system is discussed. Spoofing techniques are discussed on the basis of cooperative and non-cooperative methods so that the anti-spoofing tech

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El Beqqal, Mohamed, Mostafa Azizi, and Jean Louis Lanet. "Multimodal access control system combining RFID, fingerprint and facial recognition." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 1 (2020): 405. http://dx.doi.org/10.11591/ijeecs.v20.i1.pp405-413.

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<span>Monomodal biometry does not constitute an effective measure to meet the desired performance requirements for large-scale applications, due to limita-tions such as noisy data, restricted degree of freedom and unacceptable error rates. Some of these problems can be solved through multimodal biometric systems that involve using a combination of two or more biometric modali-ties in a single identification system. Identification based on multiple biomet-rics represents an emerging trend. The reason for combining different modal-ities is to improve the recognition rate. In practice, mult

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Sheeba, Praveen, and Gautam Reshma. "Biometric Identification using Facial Vein Patterns." International Journal of Engineering and Management Research 15, no. 1 (2025): 71–76. https://doi.org/10.5281/zenodo.14934735.

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Biometric systems play a crucial role in personal identification, leveraging the reliability and distinctiveness of physiological or behavioral traits. Among these, vein patterns in the face have gained attention for their stability and security, offering a robust method for biometric identification. This paper focuses on advancing the field with a Face Veins Based MCMT Technique. This technique utilizes a Multi-Channel Multi-Threshold approach to enhance accuracy and reliability in identifying individuals based on their unique vein patterns. By exploring the development and application of thi

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Paul, Padma P., and Marina L. Gavrilova. "A Novel Cross Folding Algorithm for Multimodal Cancelable Biometrics." International Journal of Software Science and Computational Intelligence 4, no. 3 (2012): 20–37. http://dx.doi.org/10.4018/jssci.2012070102.

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Multimodal biometric systems have emerged as highly successful new approach to combat problems of unimodal biometric system such as intraclass variability, interclass similarity, data quality, non-universality, and sensitivity to noise. However, one major issue pertinent to unimodal system remains, which has to do with actual biometric characteristics of users being permanent and their number being limited. Thus, if a user’s biometric is compromised, it might be impossible or highly difficult to replace it in a particular system. The concept of cancelable biometric or cancelability is to trans

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El-Rahiem, Basma Abd, Mohamed Amin, Ahmed Sedik, Fathi E. Abd El Samie, and Abdullah M. Iliyasu. "An efficient multi-biometric cancellable biometric scheme based on deep fusion and deep dream." Journal of Ambient Intelligence and Humanized Computing 13, no. 4 (2021): 2177–89. http://dx.doi.org/10.1007/s12652-021-03513-1.

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Allano, Lorène, Bernadette Dorizzi, and Sonia Garcia-Salicetti. "A new protocol for multi-biometric systems' evaluation maintaining the dependencies between biometric scores." Pattern Recognition 45, no. 1 (2012): 119–27. http://dx.doi.org/10.1016/j.patcog.2011.07.001.

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Paul, Padma P., and Marina L. Gavrilova. "Cancelable Fusion of Face and Ear for Secure Multi-Biometric Template." International Journal of Cognitive Informatics and Natural Intelligence 7, no. 3 (2013): 80–94. http://dx.doi.org/10.4018/ijcini.2013070105.

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Biometric fusion to achieve multimodality has emerged as a highly successful new approach to combat problems of unimodal biometric system such as intraclass variability, interclass similarity, data quality, non-universality, and sensitivity to noise. The authors have proposed new type of biometric fusion called cancelable fusion. The idea behind the cancelable biometric or cancelability is to transform a biometric data or feature into a new one so that the stored biometric template can be easily changed in a biometric security system. Cancelable fusion does the fusion of multiple biometric tra

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Mehraj, Haider, and Ajaz Hussain Mir. "A Multi-Biometric System Based on Multi-Level Hybrid Feature Fusion." Herald of the Russian Academy of Sciences 91, no. 2 (2021): 176–96. http://dx.doi.org/10.1134/s1019331621020039.

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Priya, A. Saravana, and Dr Rajeswari Mukesh. "GA based Feature Selection for Multimodal Biometric Authentication." Indian Journal of Computer Science and Engineering 12, no. 2 (2021): 526–38. http://dx.doi.org/10.21817/indjcse/2021/v12i2/211202163.

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Multi-modal biometric authentication effectively replaces uni-modal biometric authentication system towards addressing a wide range of technical glitches in identity management and authentication. Legitimacy is playing a vital role in banking, military, and healthcare sectors where highly secure, strategic and confidential data transmission is involved. By integrating many independent biometric systems, one can overcome the problems of spoofing. However, there is lack of a simple, efficient and sufficient biometric authentication. Hence, the present study focuses on designing and implementing

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OMOLOLA AKINOLA. "Robust authentication mechanisms integrating biometrics and AI for securing remote access to cloud-based healthcare services." World Journal of Advanced Research and Reviews 23, no. 3 (2024): 034–44. http://dx.doi.org/10.30574/wjarr.2024.23.3.2642.

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Strong authentication is needed to protect remote patient health data in cloud-based healthcare services. Traditional verification techniques like passwords or tokens are hard to remember, share, or steal. This project aims to secure online cloud healthcare platform access using biometrics and AI. Biometrics is the finest cloud healthcare user authentication option, while others have issues with utility, accuracy, and fraud. Research reveals that biometric approaches may authenticate persons well, but cloud storage can compromise privacy. Machine learning aids biometric recognition, but curren

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OMOLOLA, AKINOLA. "Robust authentication mechanisms integrating biometrics and AI for securing remote access to cloud-based healthcare services." World Journal of Advanced Research and Reviews 23, no. 3 (2024): 034–44. https://doi.org/10.5281/zenodo.14908946.

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Strong authentication is needed to protect remote patient health data in cloud-based healthcare services. Traditional verification techniques like passwords or tokens are hard to remember, share, or steal. This project aims to secure online cloud healthcare platform access using biometrics and AI. Biometrics is the finest cloud healthcare user authentication option, while others have issues with utility, accuracy, and fraud. Research reveals that biometric approaches may authenticate persons well, but cloud storage can compromise privacy. Machine learning aids biometric recognition, but curren

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Herbadji, Abderrahmane, Noubeil Guermat, Lahcene Ziet, Zahid Akhtar, Mohamed Cheniti, and Djamel Herbadji. "Contactless Multi-biometric System Using Fingerprint and Palmprint Selfies." Traitement du Signal 37, no. 6 (2020): 889–97. http://dx.doi.org/10.18280/ts.370602.

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Due to the COVID-19 pandemic, automated contactless person identification based on the human hand has become very vital and an appealing biometric trait. Since, people are expected to cover their faces with masks, and advised avoiding touching surfaces. It is well-known that usually contact-based hand biometrics suffer from issues like deformation due to uneven distribution of pressure or improper placement on sensor, and hygienic concerns. Whereas, to mitigate such problems, contactless imaging is expected to collect the hand biometrics information without any deformation and leading to highe

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Fei, Lunke, Bob Zhang, Chunwei Tian, Shaohua Teng, and Jie Wen. "Jointly learning multi-instance hand-based biometric descriptor." Information Sciences 562 (July 2021): 1–12. http://dx.doi.org/10.1016/j.ins.2021.01.086.

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Yun, Sung-Hyun. "The Biometric based Convertible Undeniable Multi-Signature Scheme." Journal of the Korea Academia-Industrial cooperation Society 11, no. 5 (2010): 1670–76. http://dx.doi.org/10.5762/kais.2010.11.5.1670.

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Naidu, P. Appala. "Fingerprint and Palmprint Multi-Modal Biometric Security System." International Journal of Engineering and Applied Computer Science 02, no. 05 (2017): 165–71. http://dx.doi.org/10.24032/ijeacs/0205/04.

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Kaur, Jaspreet, and Rajdeep Singh Sohal. "Multi Sensor based Biometric System using Image Processing." Research Journal of Engineering and Technology 8, no. 1 (2017): 53. http://dx.doi.org/10.5958/2321-581x.2017.00009.5.

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