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Journal articles on the topic 'Multibiometric systems'

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Published: 4 June 2021
Last updated: 5 February 2022

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1

Jain, Anil K., and Arun Ross. "Multibiometric systems." Communications of the ACM 47, no. 1 (January 1, 2004): 34. http://dx.doi.org/10.1145/962081.962102.

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2

Nair, Suresh Kumar Ramachandran, Bir Bhanu, Subir Ghosh, and Ninad S. Thakoor. "Predictive models for multibiometric systems." Pattern Recognition 47, no. 12 (December 2014): 3779–92. http://dx.doi.org/10.1016/j.patcog.2014.05.020.

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3

AlMahafzah, Harbi, and Maen Zaid AlRwashdeh. "A Survey of Multibiometric Systems." International Journal of Computer Applications 43, no. 15 (April 30, 2012): 36–43. http://dx.doi.org/10.5120/6182-8612.

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4

Li, Yong, Jian Ping Yin, and En Zhu. "An Evaluation Survey of Score Normalization in Multibiometric Systems." Advanced Engineering Forum 1 (September 2011): 168–72. http://dx.doi.org/10.4028/www.scientific.net/aef.1.168.

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Multibiometric fusion is an active research area for many years. Score normalization is to transform the scores from different matchers to a common domain. In this paper, we give a survey of classical score normalization techniques and recent advances of this research area. The performance of different normalization functions, such as MinMax, Tanh, Zscore, PL, LTL, RHE and FF are evaluated in XM2VTS Benchmark. We evaluated the performance with four different measures of biometric systems such as EER, AUC, GAR(FAR=0.001) and the threshold of EER. The experimental results show that there is no single normalization technique that would perform the best for all multibiometric recognition systems. PL and FF normalization outperform other methods in many applications.
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5

NisarBhat, Asra, and Supreet Kaur. "Enhancement of Biometric Template Security in Multibiometric Systems." International Journal of Computer Applications 69, no. 10 (May 17, 2013): 36–41. http://dx.doi.org/10.5120/11882-7698.

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6

Canuto, Anne Magaly de Paula, Michael C. Fairhurst, and Fernando Pintro. "Ensemble systems and cancellable transformations for multibiometric‐based identification." IET Biometrics 3, no. 1 (March 2014): 29–40. http://dx.doi.org/10.1049/iet-bmt.2012.0032.

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7

Herbadji, Abderrahmane, Zahid Akhtar, Kamran Siddique, Noubeil Guermat, Lahcene Ziet, Mohamed Cheniti, and Khan Muhammad. "Combining Multiple Biometric Traits Using Asymmetric Aggregation Operators for Improved Person Recognition." Symmetry 12, no. 3 (March 10, 2020): 444. http://dx.doi.org/10.3390/sym12030444.

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Biometrics is a scientific technology to recognize a person using their physical, behavior or chemical attributes. Biometrics is nowadays widely being used in several daily applications ranging from smart device user authentication to border crossing. A system that uses a single source of biometric information (e.g., single fingerprint) to recognize people is known as unimodal or unibiometrics system. Whereas, the system that consolidates data from multiple biometric sources of information (e.g., face and fingerprint) is called multimodal or multibiometrics system. Multibiometrics systems can alleviate the error rates and some inherent weaknesses of unibiometrics systems. Therefore, we present, in this study, a novel score level fusion-based scheme for multibiometric user recognition system. The proposed framework is hinged on Asymmetric Aggregation Operators (Asym-AOs). In particular, Asym-AOs are estimated via the generator functions of triangular norms (t-norms). The extensive set of experiments using seven publicly available benchmark databases, namely, National Institute of Standards and Technology (NIST)-Face, NIST-Multimodal, IIT Delhi Palmprint V1, IIT Delhi Ear, Hong Kong PolyU Contactless Hand Dorsal Images, Mobile Biometry (MOBIO) face, and Visible light mobile Ocular Biometric (VISOB) iPhone Day Light Ocular Mobile databases have been reported to show efficacy of the proposed scheme. The experimental results demonstrate that Asym-AOs based score fusion schemes not only are able to increase authentication rates compared to existing score level fusion methods (e.g., min, max, t-norms, symmetric-sum) but also is computationally fast.
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8

Hariri, Mahdi. "Possibility of spoof attack against robustness of multibiometric authentication systems." Optical Engineering 50, no. 7 (July 1, 2011): 079001. http://dx.doi.org/10.1117/1.3599874.

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9

Biggio, Battista, Giorgio Fumera, Gian Luca Marcialis, and Fabio Roli. "Statistical Meta-Analysis of Presentation Attacks for Secure Multibiometric Systems." IEEE Transactions on Pattern Analysis and Machine Intelligence 39, no. 3 (March 1, 2017): 561–75. http://dx.doi.org/10.1109/tpami.2016.2558154.

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10

Roy, Kaushik, Brian O'Connor, Foysal Ahmad, and Mohamed S. Kamel. "Multibiometric System Using Level Set, Modified LBP and Random Forest." International Journal of Image and Graphics 14, no. 03 (July 2014): 1450013. http://dx.doi.org/10.1142/s0219467814500132.

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Multibiometric systems alleviate some of the shortcomings possessed by the unimodal biometrics and provide better recognition performance. This paper presents a multibiometric system that integrates the iris and face features based on the fusion at the feature level. The proposed multibiometric system has three novelties as compared to the previous works. First, distance regularized level-set evolution (DRLSE) technique is utilized to localize the iris and pupil boundary from an iris image. The DRLSE maintains the regularity of the level set function intrinsically during the curve evolution process and increases the numerical accuracy substantially. The proposed iris localization scheme is robust against poor localization and weak iris/sclera boundaries. Second, a modified local binary pattern (MLBP), which combines both the sign and magnitude features for the improvement of recognition performance, is applied. Third, to select the optimal subset of features from the fused feature vector, a feature subset selection scheme based on random forest (RF) is proposed. To evaluate the performance of the proposed scheme, the facial images of Yale Extended B Face database are fused with the iris images of CASIA V4 interval dataset to construct an iris–face multimodal biometric dataset. The experimental results indicate that the proposed multimodal biometrics system is more reliable and robust than the unimodal biometric scheme.
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11

Paś, Jacek. "Diagnostic station for a multibiometric system for a selected transport object." AUTOBUSY – Technika, Eksploatacja, Systemy Transportowe 19, no. 12 (December 31, 2018): 585–88. http://dx.doi.org/10.24136/atest.2018.457.

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Multibiometric systems used in transport objects, in contradistinction to "ordinary" biometric systems, use several recognition techniques, e.g. fingerprint, iris, voice or face. Biometric devices are sometimes part of electronic security systems. These systems are currently installed in many transport facilities - stationary and non-stationary where there is a lot of personal traffic. These devices are most often used in extensive areas, airports, logistics bases or railway stations. The article presents issues concerning the diagnostic position for a biometric system which has in its structure several simple identification techniques.
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12

Imran, Mohammad, Ashok Rao, and G. Hemantha Kumar. "Multibiometric systems: A comparative study of multi-algorithmic and multimodal approaches." Procedia Computer Science 2 (2010): 207–12. http://dx.doi.org/10.1016/j.procs.2010.11.026.

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13

Modak, Sandip Kumar Singh, and Vijay Kumar Jha. "Multibiometric fusion strategy and its applications: A review." Information Fusion 49 (September 2019): 174–204. http://dx.doi.org/10.1016/j.inffus.2018.11.018.

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14

Kıllıoğlu, Mehmet, Murat Taşkıran, and Nihan Kahraman. "Secure data transmission for multibiometric identity verification systems using steganography and encryption." Pamukkale University Journal of Engineering Sciences 24, no. 2 (2018): 173–79. http://dx.doi.org/10.5505/pajes.2016.39225.

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15

Wu, Zhendong, Jiajia Yang, Jianwu Zhang, and Hengli Yue. "Multibiometric Fusion Authentication in Wireless Multimedia Environment Using Dynamic Bayesian Method." Security and Communication Networks 2018 (November 18, 2018): 1–12. http://dx.doi.org/10.1155/2018/5783976.

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Single biometric method has been widely used in the field of wireless multimedia authentication. However, it is vulnerable to spoofing and limited accuracy. To tackle this challenge, in this paper, we propose a multimodal fusion method for fingerprint and voiceprint by using a dynamic Bayesian method, which takes full advantage of the feature specificity extracted by a single biometrics project and authenticates users at the decision-making level. We demonstrate that this method can be extended to more modal biometric authentication and can achieve flexible accuracy of the authentication. The experiment of the method shows that the recognition rate and stability have been greatly improved, which achieves 4.46% and 5.94%, respectively, compared to the unimodal. Furthermore, it also increases 1.94% when compared with general multimodal methods for the biometric fusion recognition.
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16

Thanki, Rohit M., and Komal Rajendrakumar Borisagar. "Securing Multiple Biometric Data Using SVD and Curvelet-Based Watermarking." International Journal of Information Security and Privacy 12, no. 4 (October 2018): 35–53. http://dx.doi.org/10.4018/ijisp.2018100103.

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The security and privacy of biometric data in multibiometric systems has become a hot research topic. In this paper, a singular value decomposition (SVD) and fast discrete curvelet transform (FDCuT)-based watermarking scheme for authenticity of fingerprint image using watermark speech signal has been proposed and analyzed. This scheme also provides security to watermark speech signal, which is inserted into the fingerprint image. This proposed scheme has a number of steps including fingerprint image authentication using watermark speech signal. The human speech signal is taken as secret watermark information and inserting into the human fingerprint image in the proposed scheme. The singular value of high frequency curvelet coefficients of the host fingerprint image is modified according to watermark speech signal to get secured and watermarked fingerprint image. The analysis results show that the performance of fingerprint recognition system is not affected by inserted watermark speech signal into host fingerprint image.
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17

Masood, Isma, Yongli Wang, Ali Daud, Naif Radi Aljohani, and Hassan Dawood. "Towards Smart Healthcare: Patient Data Privacy and Security in Sensor-Cloud Infrastructure." Wireless Communications and Mobile Computing 2018 (November 4, 2018): 1–23. http://dx.doi.org/10.1155/2018/2143897.

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Nowadays, wireless body area networks (WBANs) systems have adopted cloud computing (CC) technology to overcome limitations such as power, storage, scalability, management, and computing. This amalgamation of WBANs systems and CC technology, as sensor-cloud infrastructure (S-CI), is aiding the healthcare domain through real-time monitoring of patients and the early diagnosis of diseases. Hence, the distributed environment of S-CI presents new threats to patient data privacy and security. In this paper, we review the techniques for patient data privacy and security in S-CI. Existing techniques are classified as multibiometric key generation, pairwise key establishment, hash function, attribute-based encryption, chaotic maps, hybrid encryption, Number Theory Research Unit, Tri-Mode Algorithm, Dynamic Probability Packet Marking, and Priority-Based Data Forwarding techniques, according to their application areas. Their pros and cons are presented in chronological order. We also provide our six-step generic framework for patient physiological parameters (PPPs) privacy and security in S-CI: (1) selecting the preliminaries; (2) selecting the system entities; (3) selecting the technique; (4) accessing PPPs; (5) analysing the security; and (6) estimating performance. Meanwhile, we identify and discuss PPPs utilized as datasets and provide the performance evolution of this research area. Finally, we conclude with the open challenges and future directions for this flourishing research area.
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18

Cherrat, El mehdi, Rachid Alaoui, and Hassane Bouzahir. "Convolutional neural networks approach for multimodal biometric identification system using the fusion of fingerprint, finger-vein and face images." PeerJ Computer Science 6 (January 6, 2020): e248. http://dx.doi.org/10.7717/peerj-cs.248.

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In recent years, the need for security of personal data is becoming progressively important. In this regard, the identification system based on fusion of multibiometric is most recommended for significantly improving and achieving the high performance accuracy. The main purpose of this paper is to propose a hybrid system of combining the effect of tree efficient models: Convolutional neural network (CNN), Softmax and Random forest (RF) classifier based on multi-biometric fingerprint, finger-vein and face identification system. In conventional fingerprint system, image pre-processed is applied to separate the foreground and background region based on K-means and DBSCAN algorithm. Furthermore, the features are extracted using CNNs and dropout approach, after that, the Softmax performs as a recognizer. In conventional fingervein system, the region of interest image contrast enhancement using exposure fusion framework is input into the CNNs model. Moreover, the RF classifier is proposed for classification. In conventional face system, the CNNs architecture and Softmax are required to generate face feature vectors and classify personal recognition. The score provided by these systems is combined for improving Human identification. The proposed algorithm is evaluated on publicly available SDUMLA-HMT real multimodal biometric database using a GPU based implementation. Experimental results on the datasets has shown significant capability for identification biometric system. The proposed work can offer an accurate and efficient matching compared with other system based on unimodal, bimodal, multimodal characteristics.
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19

Maiorana, Emanuele, Gabriel Emile Hine, and Patrizio Campisi. "Hill-Climbing Attacks on Multibiometrics Recognition Systems." IEEE Transactions on Information Forensics and Security 10, no. 5 (May 2015): 900–915. http://dx.doi.org/10.1109/tifs.2014.2384735.

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20

Giot, Romain, Mohamad El-Abed, and Christophe Rosenberger. "Fast computation of the performance evaluation of biometric systems: Application to multibiometrics." Future Generation Computer Systems 29, no. 3 (March 2013): 788–99. http://dx.doi.org/10.1016/j.future.2012.02.003.

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21

Gawande, Ujwalla, Mukesh Zaveri, and Avichal Kapur. "A Novel Algorithm for Feature Level Fusion Using SVM Classifier for Multibiometrics-Based Person Identification." Applied Computational Intelligence and Soft Computing 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/515918.

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Recent times witnessed many advancements in the field of biometric and ultimodal biometric fields. This is typically observed in the area, of security, privacy, and forensics. Even for the best of unimodal biometric systems, it is often not possible to achieve a higher recognition rate. Multimodal biometric systems overcome various limitations of unimodal biometric systems, such as nonuniversality, lower false acceptance, and higher genuine acceptance rates. More reliable recognition performance is achievable as multiple pieces of evidence of the same identity are available. The work presented in this paper is focused on multimodal biometric system using fingerprint and iris. Distinct textual features of the iris and fingerprint are extracted using the Haar wavelet-based technique. A novel feature level fusion algorithm is developed to combine these unimodal features using the Mahalanobis distance technique. A support-vector-machine-based learning algorithm is used to train the system using the feature extracted. The performance of the proposed algorithms is validated and compared with other algorithms using the CASIA iris database and real fingerprint database. From the simulation results, it is evident that our algorithm has higher recognition rate and very less false rejection rate compared to existing approaches.
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22

"Incorporating Metadata in Multibiometric Score-Level Fusion: an Optimized Architecture." International Journal of Innovative Technology and Exploring Engineering 9, no. 1 (November 10, 2019): 5290–305. http://dx.doi.org/10.35940/ijitee.a4118.119119.

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This manuscript presents a review on multibiometrics using ancillary information, in addition to the main biometric data. The proposed method involves taking non-biometric information into account in the biometric recognition process to improve system performance. This ancillary information can come from the user (the skin color), the sensor (the camera flash, etc.) or the operating environment (the ambient noise). Moreover, the paper presents an extension of the adapted sequential fusion framework through a complete description of the method used for the score-level fusion architecture presented at the IEEE BioSmart 2019 Proceedings. An optimized score-level fusion architecture is proposed. An introduction of new concepts (namely “biochemical features” and “multi origin biometrics”) is also made. The first part of the paper highlights the various biometric systems developed up to now, their architecture and characteristics. Then, the manuscript discussed about multibiometrics through its advantages, its diversity and the different levels of fusion. An attention was paid to the score-level fusion before addressing the consideration of ancillary information (or metadata) in multibiometrics. Dealing with the affective computing, the influence of emotion on the performance of biometric systems is explored. Finally, a typology of biometric adaptation is discussed. As an application, the proposed methodology will implement a multibiometric system using the face, contactless fingerprint and skin color. A single sensor will be used (a camera) with two shots while the skin color will be extracted automatically from the facial image.
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23

Bayram, Kadir Sercan, and Bülent Bolat. "Multibiometric identification by using ear, face, and thermal face." EURASIP Journal on Image and Video Processing 2018, no. 1 (May 14, 2018). http://dx.doi.org/10.1186/s13640-018-0274-x.

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24

"Design of Multi-Biometrics for Fake Fingerprint Detection through Body Odor." International Journal of Recent Technology and Engineering 8, no. 4 (November 30, 2019): 9646–50. http://dx.doi.org/10.35940/ijrte.d9998.118419.

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This work proposes the modernistic multibiometric recognition system for detecting artificial fingerprints and new biometric recognition system to use it in some real-time scenarios. In the recent studies of multi-biometrics, the usage of fingerprint and body odor recognition system stays untouched. This proposed design of a multi-biometric system includes a body odor recognition system along with a fingerprint recognition system that will improve the results in terms of accuracy. The reason behind proposing this model is to detect artificial fingerprints by differentiating the odor of human skin from other materials that are employed in the preparation of artificial fingerprints. This multi-biometric system can be used in forensic labs to identify criminals and to improve the standards of security in authentication of an individual. This multi-biometric system will completely eradicate the use of fake fingerprints and this proposed work will make a remarkable place in real-time applications and the history of multi-biometric systems.
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25

Khan, Maleika Heenaye-Mamode. "A Multibiometric Hand Security System." International Journal of Signal Processing Systems, December 2016, 504–9. http://dx.doi.org/10.18178/ijsps.4.6.504-509.

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