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Статті в журналах з теми "Fingerprint Liveness Detection"

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Автор: Grafiati

Опубліковано: 14 березня 2023

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1

Jiang, Yujia, and Xin Liu. "Uniform Local Binary Pattern for Fingerprint Liveness Detection in the Gaussian Pyramid." Journal of Electrical and Computer Engineering 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/1539298.

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Анотація:

Fingerprint recognition schemas are widely used in our daily life, such as Door Security, Identification, and Phone Verification. However, the existing problem is that fingerprint recognition systems are easily tricked by fake fingerprints for collaboration. Therefore, designing a fingerprint liveness detection module in fingerprint recognition systems is necessary. To solve the above problem and discriminate true fingerprint from fake ones, a novel software-based liveness detection approach using uniform local binary pattern (ULBP) in spatial pyramid is applied to recognize fingerprint liveness in this paper. Firstly, preprocessing operation for each fingerprint is necessary. Then, to solve image rotation and scale invariance, three-layer spatial pyramids of fingerprints are introduced in this paper. Next, texture information for three layers spatial pyramids is described by using uniform local binary pattern to extract features of given fingerprints. The accuracy of our proposed method has been compared with several state-of-the-art methods in fingerprint liveness detection. Experiments based on standard databases, taken from Liveness Detection Competition 2013 composed of four different fingerprint sensors, have been carried out. Finally, classifier model based on extracted features is trained using SVM classifier. Experimental results present that our proposed method can achieve high recognition accuracy compared with other methods.

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2

NIKAM, SHANKAR BHAUSAHEB, and SUNEETA AGARWAL. "CO-OCCURRENCE PROBABILITIES AND WAVELET-BASED SPOOF FINGERPRINT DETECTION." International Journal of Image and Graphics 09, no. 02 (April 2009): 171–99. http://dx.doi.org/10.1142/s0219467809003393.

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Анотація:

Perspiration phenomenon is very significant to detect the liveness of a finger. However, it requires two consecutive fingerprints to notice perspiration, and therefore may not be suitable for real time authentications. Some other methods in the literature need extra hardware to detect liveness. To alleviate these problems, in this paper, to detect liveness a new texture-based method using only the first fingerprint is proposed. It is based on the observation that real and spoof fingerprints exhibit different texture characteristics. Textural measures based on gray level co-occurrence matrix (GLCM) are used to characterize fingerprint texture. This is based on structural, orientation, roughness, smoothness and regularity differences of diverse regions in a fingerprint image. Wavelet energy signature is also used to obtain texture details. Dimensionalities of feature sets are reduced by Sequential Forward Floating Selection (SFFS) method. GLCM texture features and wavelet energy signature are independently tested on three classifiers: neural network, support vector machine and K-nearest neighbor. Finally, two best classifiers are fused using the "Sum Rule''. Fingerprint database consisting of 185 real, 90 Fun-Doh and 150 Gummy fingerprints is created. Multiple combinations of materials are used to create casts and moulds of spoof fingerprints. Experimental results indicate that, the new liveness detection method is very promising, as it needs only one fingerprint and no extra hardware to detect vitality.

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3

Lee, Youn Kyu, Jongwook Jeong, and Dongwoo Kang. "An Effective Orchestration for Fingerprint Presentation Attack Detection." Electronics 11, no. 16 (August 11, 2022): 2515. http://dx.doi.org/10.3390/electronics11162515.

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Анотація:

Fingerprint presentation attack detection has become significant due to a wide-spread usage of fingerprint authentication systems. Well-replicated fingerprints easily spoof the authentication systems because their captured images do not differ from those of genuine fingerprints in general. While a number of techniques have focused on fingerprint presentation attack detection, they suffer from inaccuracy in determining the liveness of fingerprints and performance degradation on unknown types of fingerprints. To address existing limitations, we present a robust fingerprint presentation attack detection method that orchestrates different types of neural networks by incorporating a triangular normalization method. Our method has been evaluated on a public benchmark comprising 13,000 images with five different fake materials. The evaluation exhibited our method’s higher accuracy in determining the liveness of fingerprints as well as better generalization performance on different types of fingerprints compared to existing techniques.

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4

Babikir Adam, Edriss Eisa, and Sathesh. "Evaluation of Fingerprint Liveness Detection by Machine Learning Approach - A Systematic View." Journal of ISMAC 3, no. 1 (March 1, 2021): 16–30. http://dx.doi.org/10.36548/jismac.2021.1.002.

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Анотація:

Recently, fake fingerprint detection is a challenging task in the cyber-crime sector in any developed country. Biometric authentication is growing in many sectors such as internet banking, secret file locker, etc. There spoof fingerprint detection is an essential element that is used to detect spot-on fingerprint analysis. This article focuses on the implementation and evaluation of suitable machine learning algorithms to detect fingerprint liveness. It also includes the comparative study between Ridge-let Transform (RT) and the Machine Learning (ML) approach. This article emphasis on research and analysis of the detection of the liveness spoof fingerprint and identifies the problems in different techniques and solutions. The support vector machine (SVM) classifiers work with indiscriminate loads and confined grayscale array values. This leads to a liveness report of fingerprints for detection purposes. The SVM methodology classifies the fingerprint images among more than 50K of real and spoof fingerprint image collections based on this logic. Our proposed method achieves an overall high accuracy of detection of liveness fingerprint analysis. The ensemble classifier approach model is proving an overall efficiency rate of 90.34 % accurately classifies samples than the image recognition method with RT. This recommended method demonstrates the decrement of 2.5% error rate when compared with existing methods. The augmentation of the dataset is used to improve the accuracy to detect. Besides, it gives fake fingerprint recognition and makes available future direction.

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5

Almehmadi, Abdulaziz. "A Behavioral-Based Fingerprint Liveness and Willingness Detection System." Applied Sciences 12, no. 22 (November 11, 2022): 11460. http://dx.doi.org/10.3390/app122211460.

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Анотація:

Fingerprints have been used for decades to verify the identity of an individual for various security reasons. Attackers have developed many approaches to deceive a fingerprint verification system, ranging from the sensor level, where gummy fingers are created, to gaining access to the decision-maker level, where the decision is made based on low matching criteria. Even though fingerprint sensor-level countermeasures have developed advanced metrics to detect any attempt to dupe the system, attackers still manage to outwit a fingerprint verification system. In this paper, we present the Micro-behavioral Fingerprint Analysis System (MFAS), a system that records the micro-behavior of the user’s fingertips over time as they are placing their fingerprint on the sensor. The system captures the stream of ridges as they are formed while placed on a sensor to combat the attacks that deceive the sensor. An experiment on 24 people was conducted, wherein the fingerprints and the behavior of the fingertip as it is placed were collected. Subsequently, a gummy finger was created to try to fool the system. Further, a legitimate user was chosen to participate in an experiment that mimicked an attempt to use their fingertip unwillingly to detect coerced fingerprint placement. After applying the micro-behavior, the system reported 100% true positives and 0% false-negatives when providing legitimate vs. gummy-based fingerprints to authenticate a malicious user. The system also reported a 100% accuracy in differentiating between a voluntary and a coerced fingerprint placement. The results improve the fingerprint robustness against attacks on a fingerprint sensor by factoring in micro-behavior, thus helping to overcome fake and coerced fingerprint attacks.

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6

Guo, Yanyan, Xiangdong Fei, and Qijun Zhao. "Fingerprint Liveness Detection Using Multiple Static Features and Random Forests." International Journal of Image and Graphics 14, no. 04 (October 2014): 1450021. http://dx.doi.org/10.1142/s0219467814500211.

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Анотація:

It has been demonstrated that fingerprint recognition systems are susceptible to spoofing by presenting a well-duplicated synthetic such as a gummy finger. This paper proposes a novel software-based liveness detection approach using multiple static features. Given a fingerprint image, the static features, including fingerprint coarseness, first-order statistics and intensity-based features, are extracted. Unlike previous methods, the fingerprint coarseness is modeled as multiplicative noise rather than additive noise and is extracted by cepstral analysis. A random forest classifier is employed to select effective features among the extracted features and to differentiate fake from live fingerprints. The proposed method has been evaluated on the standard database provided in the Fingerprint Liveness Detection Competition 2009 (LivDet2009). Compared with other state-of-the-art methods, the proposed method reduces the average classification error rate by more than 20%.

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7

F.W. Onifade, Olufade, Paul Akinde, and Folasade Olubusola Isinkaye. "Circular Gabor wavelet algorithm for fingerprint liveness detection." Journal of Advanced Computer Science & Technology 9, no. 1 (January 11, 2020): 1. http://dx.doi.org/10.14419/jacst.v9i1.29908.

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Анотація:

Biometrics usage is growing daily and fingerprint-based recognition system is among the most effective and popular methods of personality identification. The conventional fingerprint sensor functions on total internal reflectance (TIR), which is a method that captures the external features of the finger that is presented to it. Hence, this opens it up to spoof attacks. Liveness detection is an anti-spoofing approach that has the potentials to identify physiological features in fingerprints. It has been demonstrated that spoof fingerprint made of gelatin, gummy and play-doh can easily deceive sensor. Therefore, the security of such sensor is not guaranteed. Here, we established a secure and robust fake-spoof fingerprint identification algorithm using Circular Gabor Wavelet for texture segmentation of the captured images. The samples were exposed to feature extraction processing using circular Gabor wavelet algorithm developed for texture segmentations. The result was evaluated using FAR which measures if a user presented is accepted under a false claimed identity. The FAR result was 0.03125 with an accuracy of 99.968% which showed distinct difference between live and spoof fingerprint.

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8

Moon, Y. S., J. S. Chen, K. C. Chan, K. So, and K. C. Woo. "Wavelet based fingerprint liveness detection." Electronics Letters 41, no. 20 (2005): 1112. http://dx.doi.org/10.1049/el:20052577.

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9

Rani, Rajneesh, and Harpreet Singh. "Fingerprint Presentation Attack Detection Using Transfer Learning Approach." International Journal of Intelligent Information Technologies 17, no. 1 (January 2021): 53–67. http://dx.doi.org/10.4018/ijiit.2021010104.

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Анотація:

In this busy world, biometric authentication methods are serving as fast authentication means. But with growing dependencies on these systems, attackers have tried to exploit these systems through various attacks; thus, there is a strong need to protect authentication systems. Many software and hardware methods have been proposed in the past to make existing authentication systems more robust. Liveness detection/presentation attack detection is one such method that provides protection against malicious agents by detecting fake samples of biometric traits. This paper has worked on fingerprint liveness detection/presentation attack detection using transfer learning for which the authors have used a pre-trained NASNetMobile model. The experiments are performed on publicly available liveness datasets LivDet 2011 and LivDet 2013 and have obtained good results as compared to state of art techniques in terms of ACE(average classification error).

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10

Drahansky, Martin, Michal Dolezel, Jan Vana, Eva Brezinova, Jaegeol Yim, and Kyubark Shim. "New Optical Methods for Liveness Detection on Fingers." BioMed Research International 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/197925.

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Анотація:

This paper is devoted to new optical methods, which are supposed to be used for liveness detection on fingers. First we describe the basics about fake finger use in fingerprint recognition process and the possibilities of liveness detection. Then we continue with introducing three new liveness detection methods, which we developed and tested in the scope of our research activities—the first one is based on measurement of the pulse, the second one on variations of optical characteristics caused by pressure change, and the last one is based on reaction of skin to illumination with different wavelengths. The last part deals with the influence of skin diseases on fingerprint recognition, especially on liveness detection.

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11

Marcialis, Gian Luca, Pietro Coli, and Fabio Roli. "Fingerprint Liveness Detection Based on Fake Finger Characteristics." International Journal of Digital Crime and Forensics 4, no. 3 (July 2012): 1–19. http://dx.doi.org/10.4018/jdcf.2012070101.

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Анотація:

The vitality detection of fingerprints is currently acknowledged as a serious issue for personal identity verification systems. This problem, raised some years ago, is related to the fact that the 3d shape pattern of a fingerprint can be reproduced using artificial materials. An image quite similar to that of true, alive, fingerprint, is derived if such “fake fingers” are submitted to an electronic scanner. Since introducing hardware dedicated to liveness detection in scanners is expensive, software-based solutions, based on image processing algorithms, have been proposed as alternative. So far, proposed approaches are based on features exploiting characteristics of a live finger (e.g., finger perspiration). Such features can be named live-based, or vitality-based features. In this paper, the authors propose and motivate the use of a novel kind of features exploiting characteristics noticed in the reproduction of fake fingers, that they named fake-based features. Then the authors propose a possibile implementation of this kind of features based on the power spectrum of the fingerprint image. The proposal is compared and integrated with several live-based features at the state-of-the-art, and shows very good liveness detection performances. Experiments are carried out on a data set much larger than commonly adopted ones, containing images from three different optical sensors.

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12

Yuan, Chengsheng, Mingyu Chen, and Ying Lv. "Fingerprint Liveness Detection Approaches: A SURVEY." International Journal of Autonomous and Adaptive Communications Systems 17, no. 3 (2024): 1. http://dx.doi.org/10.1504/ijaacs.2024.10046755.

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13

Li, Xinting, Weijin Cheng, Chengsheng Yuan, Wei Gu, Baochen Yang, and Qi Cui. "Fingerprint Liveness Detection Based on Fine-Grained Feature Fusion for Intelligent Devices." Mathematics 8, no. 4 (April 3, 2020): 517. http://dx.doi.org/10.3390/math8040517.

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Анотація:

Currently, intelligent devices with fingerprint identification are widely deployed in our daily life. However, they are vulnerable to attack by fake fingerprints made of special materials. To elevate the security of these intelligent devices, many fingerprint liveness detection (FLD) algorithms have been explored. In this paper, we propose a novel detection structure to discriminate genuine or fake fingerprints. First, to describe the subtle differences between them and take advantage of texture descriptors, three types of different fine-grained texture feature extraction algorithms are used. Next, we develop a feature fusion rule, including five operations, to better integrate the above features. Finally, those fused features are fed into a support vector machine (SVM) classifier for subsequent classification. Data analysis on three standard fingerprint datasets indicates that the performance of our method outperforms other FLD methods proposed in recent literature. Moreover, data analysis results of blind materials are also reported.

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14

Agarwal, Rohit, A. S. Jalal, and K. V. Arya. "A review on presentation attack detection system for fake fingerprint." Modern Physics Letters B 34, no. 05 (February 3, 2020): 2030001. http://dx.doi.org/10.1142/s021798492030001x.

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Анотація:

Fingerprint recognition systems are susceptible to artificial spoof fingerprint attacks, like molds manufactured from polymer, gelatin or Play-Doh. Presentation attack is an open issue for fingerprint recognition systems. In a presentation attack, synthetic fingerprint which is reproduced from a real user is submitted for authentication. Different sensors are used to capture the live and fake fingerprint images. A liveness detection system has been designed to defeat different classes of spoof attacks by differentiating the features of live and fake fingerprint images. In the past few years, many hardware- and software-based approaches are suggested by researchers. However, the issues still remain challenging in terms of robustness, effectiveness and efficiency. In this paper, we explore all kinds of software-based solution to differentiate between real and fake fingerprints and present a comprehensive survey of efforts in the past to address this problem.

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15

Nogueira, Rodrigo Frassetto, Roberto de Alencar Lotufo, and Rubens Campos Machado. "Fingerprint Liveness Detection Using Convolutional Neural Networks." IEEE Transactions on Information Forensics and Security 11, no. 6 (June 2016): 1206–13. http://dx.doi.org/10.1109/tifs.2016.2520880.

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16

Kim, Wonjun. "Fingerprint Liveness Detection Using Local Coherence Patterns." IEEE Signal Processing Letters 24, no. 1 (January 2017): 51–55. http://dx.doi.org/10.1109/lsp.2016.2636158.

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17

Sharma, Ram Prakash, and Somnath Dey. "Fingerprint liveness detection using local quality features." Visual Computer 35, no. 10 (December 15, 2018): 1393–410. http://dx.doi.org/10.1007/s00371-018-01618-x.

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18

Ghiani, Luca, Abdenour Hadid, Gian Luca Marcialis, and Fabio Roli. "Fingerprint liveness detection using local texture features." IET Biometrics 6, no. 3 (January 6, 2017): 224–31. http://dx.doi.org/10.1049/iet-bmt.2016.0007.

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19

Xin, Yang, Yi Liu, Zhi Liu, Xuemei Zhu, Lingshuang Kong, Dongmei Wei, Wei Jiang, and Jun Chang. "A survey of liveness detection methods for face biometric systems." Sensor Review 37, no. 3 (June 19, 2017): 346–56. http://dx.doi.org/10.1108/sr-08-2015-0136.

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Purpose Biometric systems are widely used for face recognition. They have rapidly developed in recent years. Compared with other approaches, such as fingerprint recognition, handwriting verification and retinal and iris scanning, face recognition is more straightforward, user friendly and extensively used. The aforementioned approaches, including face recognition, are vulnerable to malicious attacks by impostors; in such cases, face liveness detection comes in handy to ensure both accuracy and robustness. Liveness is an important feature that reflects physiological signs and differentiates artificial from real biometric traits. This paper aims to provide a simple path for the future development of more robust and accurate liveness detection approaches. Design/methodology/approach This paper discusses about introduction to the face biometric system, liveness detection in face recognition system and comparisons between the different discussed works of existing measures. Originality/value This paper presents an overview, comparison and discussion of proposed face liveness detection methods to provide a reference for the future development of more robust and accurate liveness detection approaches.

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20

Zhang, Yongliang, Chenhao Gao, Shengyi Pan, Zhiwei Li, Yuanyang Xu, and Haoze Qiu. "A Score-Level Fusion of Fingerprint Matching With Fingerprint Liveness Detection." IEEE Access 8 (2020): 183391–400. http://dx.doi.org/10.1109/access.2020.3027846.

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21

Li, Jing, Yajun Chen, and Erhu Zhang. "Comprehensive edge direction descriptor for fingerprint liveness detection." Signal Processing: Image Communication 102 (March 2022): 116603. http://dx.doi.org/10.1016/j.image.2021.116603.

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22

Kim, W., and C. Jung. "Local accumulated smoothing patterns for fingerprint liveness detection." Electronics Letters 52, no. 23 (November 2016): 1912–14. http://dx.doi.org/10.1049/el.2016.3371.

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23

Gragnaniello, Diego, Giovanni Poggi, Carlo Sansone, and Luisa Verdoliva. "Local contrast phase descriptor for fingerprint liveness detection." Pattern Recognition 48, no. 4 (April 2015): 1050–58. http://dx.doi.org/10.1016/j.patcog.2014.05.021.

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24

Zhang, Yongliang, Shengyi Pan, Xiaosi Zhan, Zhiwei Li, Minghua Gao, and Chenhao Gao. "FLDNet: Light Dense CNN for Fingerprint Liveness Detection." IEEE Access 8 (2020): 84141–52. http://dx.doi.org/10.1109/access.2020.2990909.

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25

Xia, Zhihua, Rui Lv, Yafeng Zhu, Peng Ji, Huiyu Sun, and Yun-Qing Shi. "Fingerprint liveness detection using gradient-based texture features." Signal, Image and Video Processing 11, no. 2 (July 15, 2016): 381–88. http://dx.doi.org/10.1007/s11760-016-0936-z.

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26

Verma, Akhilesh, Vijay Kumar Gupta, Savita Goel, Akbar, Arun Kumar Yadav, and Divakar Yadav. "Modeling Fingerprint Presentation Attack Detection Through Transient Liveness Factor-A Person Specific Approach." Traitement du Signal 38, no. 2 (April 30, 2021): 299–307. http://dx.doi.org/10.18280/ts.380206.

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Анотація:

A self-learning, secure and independent open-set solution is essential to be explored to characterise the liveness of fingerprint presentation. Fingerprint spoof presentation classified as live (a Type-I error) is a major problem in a high-security establishment. Type-I error are manifestation of small number of spoof sample. We propose to use only live sample to overcome above challenge. We put forward an adaptive ‘fingerprint presentation attack detection’ (FPAD) scheme using interpretation of live sample. It requires initial high-quality live fingerprint sample of the concerned person. It uses six different image quality metrics as a transient attribute from each live sample and record it as ‘Transient Liveness Factor’ (TLF). Our study also proposes to apply fusion rule to validate scheme with three outlier detection algorithms, one-class support vector machine (SVM), isolation forest and local outlier factor. Proposed study got phenomenal accuracy of 100% in terms of spoof detection, which is an open-set method. Further, this study proposes and discuss open issues on person specific spoof detection on cloud-based solutions.

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27

WAYNE ASERA, Asera, and Masayoshi ARITSUGI. "Weber Centralized Binary Fusion Descriptor for Fingerprint Liveness Detection." IEICE Transactions on Information and Systems E102.D, no. 7 (July 1, 2019): 1422–25. http://dx.doi.org/10.1587/transinf.2019edl8044.

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28

Chougule, A. M., and M. A. Shah. "Use of Convolutional Neural Network for Fingerprint Liveness Detection." International Journal of Computer Sciences and Engineering 7, no. 5 (May 31, 2019): 829–32. http://dx.doi.org/10.26438/ijcse/v7i5.829832.

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29

Sequeira, Ana, and Jaime Cardoso. "Fingerprint Liveness Detection in the Presence of Capable Intruders." Sensors 15, no. 6 (June 19, 2015): 14615–38. http://dx.doi.org/10.3390/s150614615.

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30

Ametefe, D. S., S. S. Sarnin, D. M. Ali, and M. Z. Zaheer. "Fingerprint Liveness Detection Schemes: A Review on Presentation Attack." Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 10, no. 2 (January 6, 2022): 217–40. http://dx.doi.org/10.1080/21681163.2021.2012826.

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31

Yuan, Chengsheng, Zhihua Xia, Xingming Sun, Decai Sun, and Rui Lv. "Fingerprint liveness detection using multiscale difference co-occurrence matrix." Optical Engineering 55, no. 6 (June 29, 2016): 063111. http://dx.doi.org/10.1117/1.oe.55.6.063111.

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32

Toosi, Amirhosein, Andrea Bottino, Sandro Cumani, Pablo Negri, and Pietro Luca Sottile. "Feature Fusion for Fingerprint Liveness Detection: a Comparative Study." IEEE Access 5 (2017): 23695–709. http://dx.doi.org/10.1109/access.2017.2763419.

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33

Park, Eunsoo, Weonjin Kim, Qiongxiu Li, Jungmin Kim, and Hakil Kim. "Fingerprint Liveness Detection Using Patch-Based Convolutional Neural Networks." Journal of the Korea Institute of Information Security and Cryptology 27, no. 1 (February 28, 2017): 39–47. http://dx.doi.org/10.13089/jkiisc.2017.27.1.39.

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34

Sandouka, Soha B., Yakoub Bazi, and Naif Alajlan. "Transformers and Generative Adversarial Networks for Liveness Detection in Multitarget Fingerprint Sensors." Sensors 21, no. 3 (January 20, 2021): 699. http://dx.doi.org/10.3390/s21030699.

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Анотація:

Fingerprint-based biometric systems have grown rapidly as they are used for various applications including mobile payments, international border security, and financial transactions. The widespread nature of these systems renders them vulnerable to presentation attacks. Hence, improving the generalization ability of fingerprint presentation attack detection (PAD) in cross-sensor and cross-material setting is of primary importance. In this work, we propose a solution based on a transformers and generative adversarial networks (GANs). Our aim is to reduce the distribution shift between fingerprint representations coming from multiple target sensors. In the experiments, we validate the proposed methodology on the public LivDet2015 dataset provided by the liveness detection competition. The experimental results show that the proposed architecture yields an increase in average classification accuracy from 68.52% up to 83.12% after adaptation.

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35

Saeed, Sania, Hassan Dawood, Rubab Mehboob, and Hussain Dawood. "Integration of Probability Based Ridge Variation Information with Local Ridge Orientation for Fingerprint Liveness Detection." Vol 4 Issue 1 4, no. 1 (February 27, 2022): 189–200. http://dx.doi.org/10.33411/ijist/2022040114.

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Fingerprints are commonly used in biometric systems. However, the authentication of these systems became an open challenge because fingerprints can easily be fabricated. In this paper, a hybrid feature extraction approach named Integration of Probability Weighted Spatial Gradient with Ridge Orientation (IPWSGRo) has been proposed for fingerprint liveness detection. IPWSGRo integrates intensity variation and local ridge orientation information. Intensity variation is computed by using probability-weighted moments (PWM) and second order directional derivative filter. Moreover, the ridge orientation is estimated using rotation invariant Local Phase Quantization (LPQri) by retaining only the significant frequency components. These two feature vectors are quantized into predefined intervals to plot a 2-D histogram. The support vector machine classifier (SVM) is then used to determine the validity of fingerprints as either live or spoof. Results are obtained by applying the proposed technique on three standard databases of LivDet competition 2011, 2013, and 2015. Experimental results indicate that the proposed method is able to reduce the average classification error rates (ACER) to 5.7, 2.1, and 5.17% on LivDet2011, 2013, and 2015, respectively.

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36

Habib, A., and A. Selwal. "Robust anti-spoofing techniques for fingerprint liveness detection: A Survey." IOP Conference Series: Materials Science and Engineering 1033 (January 19, 2021): 012026. http://dx.doi.org/10.1088/1757-899x/1033/1/012026.

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37

Kim, Wonjun, Sungjoo Suh, Youngsung Kim, and Changkyu Choi. "Fingerprint Liveness Detection Using Ensemble of Local Image Quality Assessments." Electronic Imaging 2016, no. 2 (February 14, 2016): 1–6. http://dx.doi.org/10.2352/issn.2470-1173.2016.2.vipc-242.

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Xia, Zhihua, Chengsheng Yuan, Rui Lv, Xingming Sun, Neal N. Xiong, and Yun-Qing Shi. "A Novel Weber Local Binary Descriptor for Fingerprint Liveness Detection." IEEE Transactions on Systems, Man, and Cybernetics: Systems 50, no. 4 (April 2020): 1526–36. http://dx.doi.org/10.1109/tsmc.2018.2874281.

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39

Chengsheng Yuan, Xingming Sun, and Rui Lv. "Fingerprint liveness detection based on multi-scale LPQ and PCA." China Communications 13, no. 7 (July 2016): 60–65. http://dx.doi.org/10.1109/cc.2016.7559076.

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40

Nikam, Shankar Bhausaheb, and Suneeta Agarwal. "Wavelet-based multiresolution analysis of ridges for fingerprint liveness detection." International Journal of Information and Computer Security 3, no. 1 (2009): 1. http://dx.doi.org/10.1504/ijics.2009.026619.

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41

Jung, Ho Yub, Yong Seok Heo, and Soochahn Lee. "Fingerprint Liveness Detection by a Template-Probe Convolutional Neural Network." IEEE Access 7 (2019): 118986–93. http://dx.doi.org/10.1109/access.2019.2936890.

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42

Agrawal, Rohit, Anand Singh Jalal, and K. V. Arya. "Fake fingerprint liveness detection based on micro and macro features." International Journal of Biometrics 11, no. 2 (2019): 177. http://dx.doi.org/10.1504/ijbm.2019.099065.

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43

Arya, K. V., Anand Singh Jalal, and Rohit Agrawal. "Fake fingerprint liveness detection based on micro and macro features." International Journal of Biometrics 11, no. 2 (2019): 177. http://dx.doi.org/10.1504/ijbm.2019.10020089.

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44

Kim, Weon-jin, Qiong-xiu Li, Eun-soo Park, Jung-min Kim, and Hak-il Kim. "Fingerprint Liveness Detection and Visualization Using Convolutional Neural Networks Feature." Journal of the Korea Institute of Information Security and Cryptology 26, no. 5 (October 31, 2016): 1259–67. http://dx.doi.org/10.13089/jkiisc.2016.26.5.1259.

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45

Xia, Zhihua, Rui Lv, Yafeng Zhu, Peng Ji, Huiyu Sun, and Yun-Qing Shi. "Erratum to: Fingerprint liveness detection using gradient-based texture features." Signal, Image and Video Processing 11, no. 2 (August 25, 2016): 389. http://dx.doi.org/10.1007/s11760-016-0968-4.

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46

Hatture, Sanjeeva Kumar M., and Shweta Policepatil. "Masquerade Attack Analysis for Secured Face Biometric System." International Journal of Recent Technology and Engineering (IJRTE) 10, no. 2 (July 30, 2021): 225–32. http://dx.doi.org/10.35940/ijrte.b6309.0710221.

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Biometrics systems are mostly used to establish an automated way for validating or recognising a living or nonliving person's identity based on physiological and behavioural features. Now a day’s biometric system has become trend in personal identification for security purpose in various fields like online banking, e-payment, organizations, institutions and so on. Face biometric is the second largest biometric trait used for unique identification while fingerprint is being the first. But face recognition systems are susceptible to spoof attacks made by nonreal faces mainly known as masquerade attack. The masquerade attack is performed using authorized users’ artifact biometric data that may be artifact facial masks, photo or iris photo or any latex finger. This type of attack in Liveness detection has become counter problem in the today's world. To prevent such spoofing attack, we proposed Liveness detection of face by considering the countermeasures and texture analysis of face and also a hybrid approach which combine both passive and active liveness detection is used. Our proposed approach achieves accuracy of 99.33 percentage for face anti-spoofing detection. Also we performed active face spoofing by providing several task (turn face left, turn face right, blink eye, etc) that performed by user on live camera for liveness detection.

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47

Oladimeji, Ismaila W., Omidiora E. Olusayo, Ismaila Folasade M., and Falohun Adeleye S.. "Multi-Level Access Control System in Automated Teller Machines." International Journal of Computer Science and Mobile Computing 10, no. 4 (April 30, 2021): 146–55. http://dx.doi.org/10.47760/ijcsmc.2021.v10i04.020.

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E-commerce theft involves using lost/stolen debit/credit cards, forging checks, misleading accounting practices, etc. Due to carelessness of cardholders and criminality activities of fraudsters, the personal identification number (PIN) and using account level based fraud detection techniques methods are inadequate to cub the activities of fraudsters. In recent times, researchers have made efforts of improving cyber-security by employing biometrics traits based security system for authentication. This paper proposed a multi-level fraud detection system in automated teller machine (ATM) operations. The system included PIN level, account-level and biometric level. Acquired RealScan-F scanner was used to capture liveness fingers. Transactional data were generated for each individual fingerprint with unique PIN. The results of the simulation showed that (i) the classification at account level only yielded averages 84.3% precision, 94.5% accuracy and 5.25% false alarm rate; (ii) matching at biometric level using liveness fingerprints samples yielded 0% APCER , 0% NPCER and 100% accuracy better than using fingerprints samples that produced 4.25% APCER , 2.33% NPCER and 93.42% accuracy; (iii) combining the three levels with the condition that all the levels must be positive produced 87.5% precision,84.9% accuracy and 2.65% false alarm rate; (iv) while the classification using voting technique yielded 99.15% precision, 97.35% accuracy and 0.47% false alarm.

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Hamdan, Yasir Babiker, and A. Sathesh. "Construction of Efficient Smart Voting Machine with Liveness Detection Module." September 2021 3, no. 3 (September 13, 2021): 255–68. http://dx.doi.org/10.36548/jiip.2021.3.007.

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Voting is now governed by regulations that specify how a person's choices may be communicated and their desires can be realized. This study proposes an electronic voting machine (EVM) as an alternative for traditional voting methods, which may include the manual utilization of only microcontroller-based circuits. With the identified fingerprint liveness, the proposed technique will make voting considerably easier, more effective, and less likely to result in fraud. The suggested model will support and advance the trustworthiness of all votes and it will also assist in streamlining the counting and verification process. It is difficult to demonstrate that an advanced voting system has been properly designed since several critical criteria must be satisfied. Poll results should be kept private in the database in order to preserve the data. The voting process must also show the votes obtained by the respective candidates. The proposed authenticated voting machine can be applied to the local area elections in order to speed up the process and make the election process more transparent. To maintain its theoretical strength, the proposed research idea needs further study. The model employs radio frequency and fingerprint recognition to maintain the protection.

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Yuan, Chengsheng, Zhihua Xia, Leqi Jiang, Yi Cao, Q. M. Jonathan Wu, and Xingming Sun. "Fingerprint Liveness Detection Using an Improved CNN With Image Scale Equalization." IEEE Access 7 (2019): 26953–66. http://dx.doi.org/10.1109/access.2019.2901235.

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50

Marasco, Emanuela, and Carlo Sansone. "Combining perspiration- and morphology-based static features for fingerprint liveness detection." Pattern Recognition Letters 33, no. 9 (July 2012): 1148–56. http://dx.doi.org/10.1016/j.patrec.2012.01.009.

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