Relevant bibliographies by topics / About Fingerprint Recognition

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'About Fingerprint Recognition'

Author: Grafiati
Published: 4 June 2021
Last updated: 12 February 2022

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Journal articles on the topic "About Fingerprint Recognition"

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Marák, Pavol, and Alexander Hambalík. "Fingerprint Recognition System Using Artificial Neural Network as Feature Extractor: Design and Performance Evaluation." Tatra Mountains Mathematical Publications 67, no. 1 (September 1, 2016): 117–34. http://dx.doi.org/10.1515/tmmp-2016-0035.

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Abstract Performance of modern automated fingerprint recognition systems is heavily influenced by accuracy of their feature extraction algorithm. Nowadays, there are more approaches to fingerprint feature extraction with acceptable results. Problems start to arise in low quality conditions where majority of the traditional methods based on analyzing texture of fingerprint cannot tackle this problem so effectively as artificial neural networks. Many papers have demonstrated uses of neural networks in fingerprint recognition, but there is a little work on using them as Level-2 feature extractors. Our goal was to contribute to this field and develop a novel algorithm employing neural networks as extractors of discriminative Level-2 features commonly used to match fingerprints. In this work, we investigated possibilities of incorporating artificial neural networks into fingerprint recognition process, implemented and documented our own software solution for fingerprint identification based on neural networks whose impact on feature extraction accuracy and overall recognition rate was evaluated. The result of this research is a fully functional software system for fingerprint recognition that consists of fingerprint sensing module using high resolution sensor, image enhancement module responsible for image quality restoration, Level-1 and Level-2 feature extraction module based on neural network, and finally fingerprint matching module using the industry standard BOZORTH3 matching algorithm. For purposes of evaluation we used more fingerprint databases with varying image quality, and the performance of our system was evaluated using FMR/FNMR and ROC indicators. From the obtained results, we may draw conclusions about a very positive impact of neural networks on overall recognition rate, specifically in low quality.
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Lee, Ha-Young, and Jung-Gyu Kim. "Quality Evaluation Model about Efficiency for Fingerprint Recognition System." Journal of Digital Convergence 12, no. 6 (June 28, 2014): 215–21. http://dx.doi.org/10.14400/jdc.2014.12.6.215.

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Dey, Swarnadip, Sajal Kumar Karmakar, Surajit Goon, and Prianka Kundu. "A SURVEY ON FINGERPRINT PATTERN RECOGNITION." International Journal of Research -GRANTHAALAYAH 7, no. 8 (August 31, 2019): 496–506. http://dx.doi.org/10.29121/granthaalayah.v7.i8.2019.704.

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In this advance technical time, we all need accuracy to any security system. Among all security system, biometric recognition process is very popular in that time. Not only security purpose, identification is the main cause of using biometric characteristic. A pin, password combination is not enough to secure all things because that’s tracking is possible, but a person biometric characteristic is unique, so it is near to impossible to by-pass. In this paper, we discuss about the fingerprint types such as arch, loop, and whorl. We also discuss how the fingerprint will be recognized; however, where this technique is used in very large scale and what is the future scope of this technique, we discuss what improvement is needed in future.
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Husseis, Anas, Judith Liu-Jimenez, and Raul Sanchez-Reillo. "The Impact of Pressure on the Fingerprint Impression: Presentation Attack Detection Scheme." Applied Sciences 11, no. 17 (August 26, 2021): 7883. http://dx.doi.org/10.3390/app11177883.

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Fingerprint recognition systems have been widely deployed in authentication and verification applications, ranging from personal smartphones to border control systems. Recently, the biometric society has raised concerns about presentation attacks that aim to manipulate the biometric system’s final decision by presenting artificial fingerprint traits to the sensor. In this paper, we propose a presentation attack detection scheme that exploits the natural fingerprint phenomena, and analyzes the dynamic variation of a fingerprint’s impression when the user applies additional pressure during the presentation. For that purpose, we collected a novel dynamic dataset with an instructed acquisition scenario. Two sensing technologies are used in the data collection, thermal and optical. Additionally, we collected attack presentations using seven presentation attack instrument species considering the same acquisition circumstances. The proposed mechanism is evaluated following the directives of the standard ISO/IEC 30107. The comparison between ordinary and pressure presentations shows higher accuracy and generalizability for the latter. The proposed approach demonstrates efficient capability of detecting presentation attacks with low bona fide presentation classification error rate (BPCER) where BPCER is 0% for an optical sensor and 1.66% for a thermal sensor at 5% attack presentation classification error rate (APCER) for both.
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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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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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Chen, Yue, Xiang Chen, and Yingke Lei. "Emitter Identification of Digital Modulation Transmitter Based on Nonlinearity and Modulation Distortion of Power Amplifier." Sensors 21, no. 13 (June 25, 2021): 4362. http://dx.doi.org/10.3390/s21134362.

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Specific transmitter identification (SEI) is a technology that uses a received signal to identify to which individual radiation source the transmitted signal belongs. It can complete the identification of the signal transmitter in a non-cooperative scenario. Therefore, there are broad application prospects in the field of wireless-communication-network security, spectral resource management, and military battlefield-target communication countermeasures. This article demodulates and reconstructs a digital modulation signal to obtain a signal without modulator distortion and power-amplifier nonlinearity. Comparing the reconstructed signal with the actual received signal, the coefficient representation of the nonlinearity of the power amplifier and the distortion of the modulator can be obtained, and these coefficients can be used as the fingerprint characteristics of different transmitters through a convolutional neural network (CNN) to complete the identification of specific transmitters. The existing SEI strategy for changing the modulation parameters of a test signal is to mix part of the test signal with the training signal so that the classifier can learn the signal of which the modulation parameter was changed. This method is still data-oriented and cannot process signals for which the classifier has not been trained. It has certain limitations in practical applications. We compared the fingerprint features extracted by the method in this study with the fingerprint features extracted by the bispectral method. When SNR < 20 dB, the recognition accuracy of the bispectral method dropped rapidly. The method in this paper still achieved 86% recognition accuracy when SNR = 0 dB. When the carrier frequency of the test signal was changed, the bispectral feature failed, and the proposed method could still achieve a recognition accuracy of about 70%. When changing the test-signal baud rate, the proposed method could still achieve a classification accuracy rate of more than 70% for four different individual radiation sources when SNR = 0 dB.
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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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Fei, Lunke, Shaohua Teng, Jigang Wu, and Imad Rida. "Enhanced Minutiae Extraction for High-Resolution Palmprint Recognition." International Journal of Image and Graphics 17, no. 04 (October 2017): 1750020. http://dx.doi.org/10.1142/s0219467817500206.

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A palmprint generally possesses about 10 times more minutiae features than a fingerprint, which could provide reliable biometric-based personal authentication. However, wide distribution of various creases in a palmprint creates a number of spurious minutiae. Precisely and efficiently, minutiae extraction is one of the most critical and challenging work for high-resolution palmprint recognition. In this paper, we propose a novel minutiae extraction and matching method for high-resolution palmprint images. The main contributions of this work include the following. First, a circle-boundary consistency is proposed to update the local ridge orientation of some abnormal points. Second, a lengthened Gabor filter is designed to better recover the discontinuous ridges corrupted by wide creases. Third, the principal ridge orientation of palmprint image is calculated to establish an angle alignment system, and coarse-to-fine shifting is performed to obtain the optimal coordinate translation parameters. Following these steps, minutiae matching can be efficiently performed. Experiment results conducted on the public high-resolution palmprint database validate the effectiveness of the proposed method.
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Hounsell, Elizabeth F., MIA Young, and Michael J. Davies. "Glycoprotein Changes in Tumours: A Renaissance in Clinical Applications." Clinical Science 93, no. 4 (October 1, 1997): 287–93. http://dx.doi.org/10.1042/cs0930287.

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1. Oligosaccharides linked to protein (glycoprotein) or lipid (glycolipid) are the major components at the outer surface of mammalian cells. Studies using antibodies and lectins have shown in the past that the oligosaccharides they recognize exhibit tumour-associated changes, i.e. they are carbohydrate tumour-associated antigens. 2. The oligosaccharides have been further characterized in recent years by structural analysis using high-resolution chromatographic techniques, MS and NMR. NMR gives an oligosaccharide fingerprint that is characteristic of monosaccharide type and linkage and which can be correlated with magnetic resonance spectroscopic data on fine-needle tissue aspirates. 3. Also of relevance is the new understanding of the molecular biology of MUC genes, which code for mucin protein backbones, and of the glycosyltransferase genes, which determine oligosaccharide structure and immunological recognition. 4. For these reasons, we believe that tumour-associated oligosaccharide changes should be revisited in the context of what we now know about structure and expression. This review synopsizes the past data using the detection of carbohydrate tumour-associated antigens by binding of lectins and antibodies, and puts it into the context of NMR fingerprints or signatures.
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Costa, Nattane, Laura Llobodanin, Inar Castro, and Rommel Barbosa. "Geographical Classification of Tannat Wines Based on Support Vector Machines and Feature Selection." Beverages 4, no. 4 (November 30, 2018): 97. http://dx.doi.org/10.3390/beverages4040097.

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Geographical product recognition has become an issue for researchers and food industries. One way to obtain useful information about the fingerprint of wines is by examining that fingerprint’s chemical components. In this paper, we present a data mining and predictive analysis to classify Brazilian and Uruguayan Tannat wines from the South region using the support vector machine (SVM) classification algorithm with the radial basis kernel function and the F-score feature selection method. A total of 37 Tannat wines differing in geographical origin (9 Brazilian samples and 28 Uruguayan samples) were analyzed. We concluded that given the use of at least one anthocyanin (peon-3-glu) and the radical scavenging activity (DPPH), the Tannat wines can be classified with 94.64% accuracy and 0.90 Matthew’s correlation coefficient (MCC). Furthermore, the combination of SVM and feature selection proved useful for determining the main chemical parameters that discriminate with regard to the origin of Tannat wines and classifying them with a high degree of accuracy. Additionally, to our knowledge, this is the first study to classify the Tannat wine variety in the context of two countries in South America.
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Dissertations / Theses on the topic "About Fingerprint Recognition"

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Jayapal, Ranjith. "Biometric encryption system for increased security." UNF Digital Commons, 2017. http://digitalcommons.unf.edu/etd/746.

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Security is very important in present day life. In this highly-interconnected world, most of our daily activities are computer based, and the data transactions are protected by passwords. These passwords identify various entities such as bank accounts, mobile phones, etc. People might reuse the same password, or passwords related to an individual that can lead to attacks. Indeed, remembering several passwords can become a tedious task. Biometrics is a science that measures an individual’s physical characteristics in a unique way. Thus, biometrics serves as a method to replace the cumbersome use of complex passwords. Our research uses the features of biometrics to efficiently implement a biometric encryption system with a high level of security.
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Book chapters on the topic "About Fingerprint Recognition"

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Raja, Rohit, Hiral Raja, RajKumar Patra, Kamal Mehta, Akanksha Gupta, and Kunta Ramya Laxmi. "Assessment Methods of Cognitive Ability of Human Brains for Inborn Intelligence Potential Using Pattern Recognitions." In Biometric Systems [Working Title]. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.93268.

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This research aims to examine the scientific study related to fingerprint patterns and brains lobes. Generally, this method is used to find and develop the inborn potential and personality especially of children. Every person is having inborn potential and personality, which will help us to analyze strength and weakness. The present work is based only on the analysis and used as a reference for scientific research in the field of Galtian and statistical study conducted based on the fingerprint processing. Human brain is divided into two parts, left hemispheres and right hemispheres. Fingers of right hand represent the functions of left brain and fingers of left hand represent the functions of right brain. Human brain is divided into 10 lobes and each lobe is related with each finger. Each lobe represents different intelligences. A detailed analysis of the fingerprint would help the researchers to find the inborn talents. It will provide them the most appropriate learning habits from young age and improve learning ability effectively. The vital factor of an individual’s intelligence is determined by neural network connection of brain cells. Cognitive science is the scientific study that will help you to know about yourself.
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Conference papers on the topic "About Fingerprint Recognition"

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Cai, Li-Jing, Li-Juan Cai, Zhan-Hua Cui, Ying-Nan Liu, and Li Yang. "The study about fingerprint recognition based on orthogonal transforms." In 2010 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2010. http://dx.doi.org/10.1109/icmlc.2010.5580773.

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