Contents
Academic literature on the topic 'Fingerprint Liveness Detection'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Fingerprint Liveness Detection.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Journal articles on the topic "Fingerprint Liveness Detection"
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.
Full textAbstract:
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.
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 (2009): 171–99. http://dx.doi.org/10.1142/s0219467809003393.
Full textAbstract:
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.
3
Lee, Youn Kyu, Jongwook Jeong, and Dongwoo Kang. "An Effective Orchestration for Fingerprint Presentation Attack Detection." Electronics 11, no. 16 (2022): 2515. http://dx.doi.org/10.3390/electronics11162515.
Full textAbstract:
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.
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 (2021): 16–30. http://dx.doi.org/10.36548/jismac.2021.1.002.
Full textAbstract:
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.
5
Almehmadi, Abdulaziz. "A Behavioral-Based Fingerprint Liveness and Willingness Detection System." Applied Sciences 12, no. 22 (2022): 11460. http://dx.doi.org/10.3390/app122211460.
Full textAbstract:
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.
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 (2014): 1450021. http://dx.doi.org/10.1142/s0219467814500211.
Full textAbstract:
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%.
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 (2020): 1. http://dx.doi.org/10.14419/jacst.v9i1.29908.
Full textAbstract:
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.
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.
Full text
9
Rani, Rajneesh, and Harpreet Singh. "Fingerprint Presentation Attack Detection Using Transfer Learning Approach." International Journal of Intelligent Information Technologies 17, no. 1 (2021): 53–67. http://dx.doi.org/10.4018/ijiit.2021010104.
Full textAbstract:
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).
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.
Full textAbstract:
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.