Relevant bibliographies by topics / Minuzie biometria

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Academic literature on the topic 'Minuzie biometria'

Author: Grafiati
Published: 4 June 2021
Last updated: 5 February 2022

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Journal articles on the topic "Minuzie biometria"

1

Singh, Law Kumar, Munish Khanna, and Hitendra Garg. "Multimodal Biometric Based on Fusion of Ridge Features with Minutiae Features and Face Features." International Journal of Information System Modeling and Design 11, no. 1 (2020): 37–57. http://dx.doi.org/10.4018/ijismd.2020010103.

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Multimodal biometrics refers to the exploiting combination of two or more biometric modalities in an identification of a system. Fingerprint, face, retina, iris, hand geometry, DNA, and palm print are physiological traits while voice, signature, keystrokes, gait are behavioural traits used for identification by a system. Single biometric features like faces, fingerprints, irises, retinas, etc., deteriorate or change with time, environment, user mode, physiological defects, and circumstance therefore integrating multi features of biometric traits increase robustness of the system. The proposed multimodal biometrics system presents recognition based on face detection and fingerprint physiological traits. This proposed system increases the efficiency, accuracy and decreases execution time of the system as compared to the existing systems. The performance of proposed method is reported in terms of parameters such as False Rejection Rate (FRR), False Acceptance Rate (FAR) and Equal Error Rate (EER) and accuracy is reported at 95.389%.
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2

Sandhya, Mulagala, and Munaga V. N. K. Prasad. "Cancelable Fingerprint Cryptosystem Using Multiple Spiral Curves and Fuzzy Commitment Scheme." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 04 (2017): 1756004. http://dx.doi.org/10.1142/s0218001417560043.

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The increased use of biometric-based authentication systems in a variety of applications has made biometric template protection an important issue. Unlike conventional systems, biometric cannot be revoked or changed. This made template protection a critical issue to be considered in the recent years. This paper proposes a cancelable fingerprint cryptosystem using multiple spiral curves and fuzzy commitment scheme. The method is built by combining cancelable biometrics and biometric cryptosystems. First, we compute transformed minutiae features using multiple spiral curves. Further, these transformed features are encrypted using fuzzy commitment scheme. Hence, a secure template is obtained. Experimental results and analysis prove the credibility of proposed method with recently presented methods of fingerprint template protection.
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3

Li, Mengxing, Quan Feng, Jian Zhao, Mei Yang, Lijun Kang, and Lili Wu. "Minutiae Matching with Privacy Protection Based on the Combination of Garbled Circuit and Homomorphic Encryption." Scientific World Journal 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/525387.

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Biometrics plays an important role in authentication applications since they are strongly linked to holders. With an increasing growth of e-commerce and e-government, one can expect that biometric-based authentication systems are possibly deployed over the open networks in the near future. However, due to its openness, the Internet poses a great challenge to the security and privacy of biometric authentication. Biometric data cannot be revoked, so it is of paramount importance that biometric data should be handled in a secure way. In this paper we present a scheme achieving privacy-preserving fingerprint authentication between two parties, in which fingerprint minutiae matching algorithm is completed in the encrypted domain. To improve the efficiency, we exploit homomorphic encryption as well as garbled circuits to design the protocol. Our goal is to provide protection for the security of template in storage and data privacy of two parties in transaction. The experimental results show that the proposed authentication protocol runs efficiently. Therefore, the protocol can run over open networks and help to alleviate the concerns on security and privacy of biometric applications over the open networks.
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4

D S, Dr Dinesh Kumar. "Human Authentication using Face, Voice and Fingerprint Biometrics." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 853–62. http://dx.doi.org/10.22214/ijraset.2021.36381.

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Multimodal biometric approaches are growing in importance for personal verification and identification, since they provide better recognition results and hence improve security compared to biometrics based on a single modality. In this project, we present a multimodal biometric system that is based on the fusion of face, voice and fingerprint biometrics. For face recognition, we employ Haar Cascade Algorithm, while minutiae extraction is used for fingerprint recognition and we will be having a stored code word for the voice authentication, if any of these two authentication becomes true, the system consider the person as authorized person. Fusion at matching score level is then applied to enhance recognition performance. In particular, we employ the product rule in our investigation. The final identification is then performed using a nearest neighbour classifier which is fast and effective. Experimental results confirm that our approach achieves excellent recognition performance, and that the fusion approach outperforms biometric identification based on single modalities.
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5

Rashid, Mofeed, and Huda Zaki. "RSA Cryptographic Key Generation Using Fingerprint Minutiae." Iraqi Journal for Computers and Informatics 41, no. 1 (2014): 66–69. http://dx.doi.org/10.25195/ijci.v41i1.101.

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Human users find difficult to remember long cryptographic keys. Therefore, researchers, for a long time period, have beeninvestigating ways to use biometric features of the user rather than memorable password or passphrase, in an attempt to produce tough andunrepeatable cryptographic keys and to construct the key unpredictable to a hacker who is deficient of important knowledge about theuser's biometrics. In this paper, generating the strong bio-crypt key based on fingerprint minutiae is presented. At first, the minutiae pointsare extracted from the fingerprint image based on image processing algorithms. Then, the extracted fingerprint minutiae are used forgenerating a 1024 bit prime numbers that used in RSA cypher algorithm to generate 2048 cryptographic key.
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6

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 is chosen as a biometric attribute as there are no parallel retina feature matches except if they are of the comparative client; likewise, retina has a decent vessel design making it a decent confirming methodology when contrasted with other biometric attributes. Security is found in the framework by multi-modular biometric combination of retina with finger vein and unique finger impression. Feature extraction approach and cryptography are utilized so as to accomplish security. The element extraction is finished with the assistance of MINUTIA and SIFT calculation, which are at that point characterized utilizing deep neural network (DNN). The element key focuses are intertwined at score level utilizing separation normal and later matched. The test result assessed utilizing MATLAB delineates the significant improvement in the presentation of multi-modular biometric frameworks with higher qualities in GAR and FAR rates.
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7

Wang, Feng. "Fusion Fingerprint and Face Multi-Biometrics Recognition Based on D-S Evidence Theory." Advanced Materials Research 459 (January 2012): 644–48. http://dx.doi.org/10.4028/www.scientific.net/amr.459.644.

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Single biometric feature has not meet people's needs. After analyzing fingerprints and face recognition technology, a fused theory is proposed which comprise fingerprints and face multi-biometrics features recognition based on D-S evidence theory. This article first analysis main part of the Face and Fingerprint Identification System, then gives a decision-making on integration in the face and fingerprint recognition method. In this paper we divide minutia features of fingerprint into certain and uncertain region which could make the performance of verification in certain region better than the original performance. By this fusion strategy the whole performance is improved.
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8

Appati, Justice Kwame, Prince Kofi Nartey, Ebenezer Owusu, and Ismail Wafaa Denwar. "Implementation of a Transform-Minutiae Fusion-Based Model for Fingerprint Recognition." International Journal of Mathematics and Mathematical Sciences 2021 (March 4, 2021): 1–12. http://dx.doi.org/10.1155/2021/5545488.

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Biometrics consists of scientific methods of using a person’s unique physiological or behavioral traits for electronic identification and verification. The traits for biometric identification are fingerprint, voice, face, and palm print recognition. However, this study considers fingerprint recognition for in-person identification since they are distinctive, reliable, and relatively easy to acquire. Despite the many works done, the problem of accuracy still persists which perhaps can be attributed to the varying characteristic of the acquisition devices. This study seeks to improve the issue recognition accuracy with the proposal of the fusion of a two transform and minutiae models. In this study, a transform-minutiae fusion-based model for fingerprint recognition is proposed. The first transform technique, thus wave atom transform, was used for data smoothing while the second transform, thus wavelet, was used for feature extraction. These features were added to the minutiae features for person recognition. Evaluating the proposed design on the FVC 2002 dataset showed a relatively better performance compared to existing methods with an accuracy measure of 100% as to 96.67% and 98.55% of the existing methods.
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9

Sharma, Uttam, Pradeep Tomar, Syed Sadaf Ali, Neetesh Saxena, and Robin Singh Bhadoria. "Optimized Authentication System with High Security and Privacy." Electronics 10, no. 4 (2021): 458. http://dx.doi.org/10.3390/electronics10040458.

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Authentication and privacy play an important role in the present electronic world. Biometrics and especially fingerprint-based authentication are extremely useful for unlocking doors, mobile phones, etc. Fingerprint biometrics usually store the attributes of the minutia point of a fingerprint directly in the database as a user template. Existing research works have shown that from such insecure user templates, original fingerprints can be constructed. If the database gets compromised, the attacker may construct the fingerprint of a user, which is a serious security and privacy issue. Security of original fingerprints is therefore extremely important. Ali et al. have designed a system for secure fingerprint biometrics; however, their technique has various limitations and is not optimized. In this paper, first we have proposed a secure technique which is highly robust, optimized, and fast. Secondly, unlike most of the fingerprint biometrics apart from the minutiae point location and orientation, we have used the quality of minutiae points as well to construct an optimized template. Third, the template constructed is in 3D shell shape. We have rigorously evaluated the technique on nine different fingerprint databases. The obtained results from the experiments are highly promising and show the effectiveness of the technique.
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10

Selvarani, P., and N. Malarvizhi. "Multibiometric authentication with MATLAB simulation." International Journal of Engineering & Technology 7, no. 1.7 (2018): 47. http://dx.doi.org/10.14419/ijet.v7i1.7.9389.

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Multimodal Biometric Authentication has been used as more security purpose for establishing the user Identification, Authentication and Verification purpose. Multimodal Biometric like Fingerprint and iris are used in this research work for authentication purpose using Matlab simulation. Fingerprint recognition process like Image Enhancement, binarization, Segmentation, thinning, Minutia marking, and Matching are performed with various techniques like Histogram Equalization, Adaptive Binarization, Morphological operations, Minutiae based techniques etc.,Iris recognition process like Segmentation, Normalization, Encoding and Matching are performed with various techniques like Canny edge detection, Daughman’s Rubber sheet model, Hamming Distance etc., can be applied for Fingerprint and iris recognition for authentication purpose. Finally Performance the measure of Precision, Recall, F-Score and Accuracy has evaluated in both fingerprint and iris. It can be concluded Iris Accuracy is higher 0.96% compared with fingerprint accuracy 0.81%.
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