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Journal articles on the topic 'Spoofed fingerprints'

Author: Grafiati

Published: 28 June 2021

Last updated: 1 February 2022

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1

Nastasiu, Dragoș, Răzvan Scripcaru, Angela Digulescu, Cornel Ioana, Raymundo De Amorim, Nicolas Barbot, Romain Siragusa, Etienne Perret, and Florin Popescu. "A New Method of Secure Authentication Based on Electromagnetic Signatures of Chipless RFID Tags and Machine Learning Approaches." Sensors 20, no. 21 (November 9, 2020): 6385. http://dx.doi.org/10.3390/s20216385.

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In this study, we present the implementation of a neural network model capable of classifying radio frequency identification (RFID) tags based on their electromagnetic (EM) signature for authentication applications. One important application of the chipless RFID addresses the counterfeiting threat for manufacturers. The goal is to design and implement chipless RFID tags that possess a unique and unclonable fingerprint to authenticate objects. As EM characteristics are employed, these fingerprints cannot be easily spoofed. A set of 18 tags operating in V band (65–72 GHz) was designed and measured. V band is more sensitive to dimensional variations compared to other applications at lower frequencies, thus it is suitable to highlight the differences between the EM signatures. Machine learning (ML) approaches are used to characterize and classify the 18 EM responses in order to validate the authentication method. The proposed supervised method reached a maximum recognition rate of 100%, surpassing in terms of accuracy most of RFID fingerprinting related work. To determine the best network configuration, we used a random search algorithm. Further tuning was conducted by comparing the results of different learning algorithms in terms of accuracy and loss.

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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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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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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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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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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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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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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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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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Lee, Fu-Yuan, and Shiuhpyng Shieh. "Defending against spoofed DDoS attacks with path fingerprint." Computers & Security 24, no. 7 (October 2005): 571–86. http://dx.doi.org/10.1016/j.cose.2005.03.005.

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7

Leghari, Mehwish, Shahzad Memon, Lachhman Das Dhomeja, Akhtar Hussain Jalbani, and Asghar Ali Chandio. "Deep Feature Fusion of Fingerprint and Online Signature for Multimodal Biometrics." Computers 10, no. 2 (February 7, 2021): 21. http://dx.doi.org/10.3390/computers10020021.

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The extensive research in the field of multimodal biometrics by the research community and the advent of modern technology has compelled the use of multimodal biometrics in real life applications. Biometric systems that are based on a single modality have many constraints like noise, less universality, intra class variations and spoof attacks. On the other hand, multimodal biometric systems are gaining greater attention because of their high accuracy, increased reliability and enhanced security. This research paper proposes and develops a Convolutional Neural Network (CNN) based model for the feature level fusion of fingerprint and online signature. Two types of feature level fusion schemes for the fingerprint and online signature have been implemented in this paper. The first scheme named early fusion combines the features of fingerprints and online signatures before the fully connected layers, while the second fusion scheme named late fusion combines the features after fully connected layers. To train and test the proposed model, a new multimodal dataset consisting of 1400 samples of fingerprints and 1400 samples of online signatures from 280 subjects was collected. To train the proposed model more effectively, the size of the training data was further increased using augmentation techniques. The experimental results show an accuracy of 99.10% achieved with early feature fusion scheme, while 98.35% was achieved with late feature fusion scheme.

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Sekulska-Nalewajko, Joanna, Jarosław Gocławski, and Dominik Sankowski. "The Detection of Internal Fingerprint Image Using Optical Coherence Tomography." Image Processing & Communications 22, no. 4 (December 1, 2017): 59–72. http://dx.doi.org/10.1515/ipc-2017-0022.

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Abstract Recently, optical coherence tomography (OCT) has been tested as a contactless technique helpful for damaged or spoofed fingerprint recovery. Three dimensional OCT images cover the range from the skin surface to papillary region in upper dermis. The proposed method extracts from cross-sections of volumetric images (B-scans) high intensity ridges in both air-epidermis and dermis-epidermis interfaces. The extraction is based on the localisation of two OCT signal peaks corresponding to these edges. The borders are spline smoothed in two orthogonal planes of the image space. The result images are presented and compared with camera views.

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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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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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Chugh, Tarang, and Anil K. Jain. "Fingerprint Spoof Detector Generalization." IEEE Transactions on Information Forensics and Security 16 (2021): 42–55. http://dx.doi.org/10.1109/tifs.2020.2990789.

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Iula, Antonio. "Ultrasound Systems for Biometric Recognition." Sensors 19, no. 10 (May 20, 2019): 2317. http://dx.doi.org/10.3390/s19102317.

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Biometric recognition systems are finding applications in more and more civilian fields because they proved to be reliable and accurate. Among the other technologies, ultrasound has the main merit of acquiring 3D images, which allows it to provide more distinctive features and gives it a high resistance to spoof attacks. This work reviews main research activities devoted to the study and development of ultrasound sensors and systems for biometric recognition purposes. Several transducer technologies and different ultrasound techniques have been experimented on for imaging biometric characteristics like fingerprints, hand vein pattern, palmprint, and hand geometry. In the paper, basic concepts on ultrasound imaging techniques and technologies are briefly recalled and, subsequently, research studies are classified according to the kind of technique used for collecting the ultrasound image. Overall, the overview demonstrates that ultrasound may compete with other technologies in the expanding market of biometrics, as the different commercial fingerprint sensors integrated in portable electronic devices like smartphones or tablets demonstrate.

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Shidik, Guruh Fajar, Edi Jaya Kusuma, Safira Nuraisha, and Pulung Nurtantio Andono. "Heuristic vs Metaheuristic Method: Improvement of Spoofed Fingerprint Identification in IoT Devices." International Review on Modelling and Simulations (IREMOS) 12, no. 3 (June 30, 2019): 168. http://dx.doi.org/10.15866/iremos.v12i3.17330.

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Arunalatha, G., and M. Ezhilarasan. "Fingerprint Spoof Detection Using Quality Features." International Journal of Security and Its Applications 9, no. 10 (October 31, 2015): 83–94. http://dx.doi.org/10.14257/ijsia.2015.9.10.07.

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Balaji, Aadhithya, Varun. H.S., and Sikha. O.K. "Multimodal Fingerprint Spoof Detection Using White Light." Procedia Computer Science 78 (2016): 330–35. http://dx.doi.org/10.1016/j.procs.2016.02.066.

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Yassin, Ali A. "Efficiency and Flexibility of Fingerprint Scheme Using Partial Encryption and Discrete Wavelet Transform to Verify User in Cloud Computing." International Scholarly Research Notices 2014 (September 24, 2014): 1–13. http://dx.doi.org/10.1155/2014/351696.

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Now, the security of digital images is considered more and more essential and fingerprint plays the main role in the world of image. Furthermore, fingerprint recognition is a scheme of biometric verification that applies pattern recognition techniques depending on image of fingerprint individually. In the cloud environment, an adversary has the ability to intercept information and must be secured from eavesdroppers. Unluckily, encryption and decryption functions are slow and they are often hard. Fingerprint techniques required extra hardware and software; it is masqueraded by artificial gummy fingers (spoof attacks). Additionally, when a large number of users are being verified at the same time, the mechanism will become slow. In this paper, we employed each of the partial encryptions of user’s fingerprint and discrete wavelet transform to obtain a new scheme of fingerprint verification. Moreover, our proposed scheme can overcome those problems; it does not require cost, reduces the computational supplies for huge volumes of fingerprint images, and resists well-known attacks. In addition, experimental results illustrate that our proposed scheme has a good performance of user’s fingerprint verification.

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Darlow, Luke Nicholas, Leandra Webb, and Natasha Botha. "Automated spoof-detection for fingerprints using optical coherence tomography." Applied Optics 55, no. 13 (April 22, 2016): 3387. http://dx.doi.org/10.1364/ao.55.003387.

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Chugh, Tarang, Kai Cao, and Anil K. Jain. "Fingerprint Spoof Buster: Use of Minutiae-Centered Patches." IEEE Transactions on Information Forensics and Security 13, no. 9 (September 2018): 2190–202. http://dx.doi.org/10.1109/tifs.2018.2812193.

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18

Jiang, Yujia, and Xin Liu. "Spoof Fingerprint Detection based on Co-occurrence Matrix." International Journal of Signal Processing, Image Processing and Pattern Recognition 8, no. 8 (August 31, 2015): 373–84. http://dx.doi.org/10.14257/ijsip.2015.8.8.38.

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Barsky, Tatiana, Ariel Tankus, and Yehezkel Yeshurun. "Classification of fingerprint images to real vs. spoof." International Journal of Biometrics 4, no. 1 (2012): 1. http://dx.doi.org/10.1504/ijbm.2012.044289.

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Kho, Jun Beom, Wonjune Lee, Heeseung Choi, and Jaihie Kim. "An incremental learning method for spoof fingerprint detection." Expert Systems with Applications 116 (February 2019): 52–64. http://dx.doi.org/10.1016/j.eswa.2018.08.055.

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Rattani, Ajita, Walter J. Scheirer, and Arun Ross. "Open Set Fingerprint Spoof Detection Across Novel Fabrication Materials." IEEE Transactions on Information Forensics and Security 10, no. 11 (November 2015): 2447–60. http://dx.doi.org/10.1109/tifs.2015.2464772.

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Pereira, L. F. A., H. N. B. Pinheiro, G. D. C. Cavalcanti, and Tsang Ing Ren. "Spatial surface coarseness analysis: technique for fingerprint spoof detection." Electronics Letters 49, no. 4 (February 2013): 260–61. http://dx.doi.org/10.1049/el.2012.4173.

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Nikam, Shankar Bhausaheb, and Suneeta Agarwal. "Local binary pattern and wavelet-based spoof fingerprint detection." International Journal of Biometrics 1, no. 2 (2008): 141. http://dx.doi.org/10.1504/ijbm.2008.020141.

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ALTUN, A. ALPASLAN, H. ERDINC KOCER, and NOVRUZ ALLAHVERDI. "GENETIC ALGORITHM BASED FEATURE SELECTION LEVEL FUSION USING FINGERPRINT AND IRIS BIOMETRICS." International Journal of Pattern Recognition and Artificial Intelligence 22, no. 03 (May 2008): 585–600. http://dx.doi.org/10.1142/s0218001408006351.

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An accuracy level of unimodal biometric recognition system is not very high because of noisy data, limited degrees of freedom, spoof attacks etc. problems. A multimodal biometric system which uses two or more biometric traits of an individual can overcome such problems. We propose a multimodal biometric recognition system that fuses the fingerprint and iris features at the feature extraction level. A feed-forward artificial neural networks (ANNs) model is used for recognition of a person. There is a need to make the training time shorter, so the feature selection level should be performed. A genetic algorithms (GAs) approach is used for feature selection of a combined data. As an experiment, the database of 60 users, 10 fingerprint images and 10 iris images taken from each person, is used. The test results are presented in the last stage of this research.

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Soum, Veasna, Sooyoung Park, Albertus Ivan Brilian, Yunpyo Kim, Madeline Y. Ryu, Taler Brazell, F. John Burpo, Kevin Kit Parker, Oh-Sun Kwon, and Kwanwoo Shin. "Inkjet-Printed Carbon Nanotubes for Fabricating a Spoof Fingerprint on Paper." ACS Omega 4, no. 5 (May 16, 2019): 8626–31. http://dx.doi.org/10.1021/acsomega.9b00936.

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Jia, Xiaofei, Xin Yang, Kai Cao, Yali Zang, Ning Zhang, Ruwei Dai, Xinzhong Zhu, and Jie Tian. "Multi-scale local binary pattern with filters for spoof fingerprint detection." Information Sciences 268 (June 2014): 91–102. http://dx.doi.org/10.1016/j.ins.2013.06.041.

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G., Arunalatha, and M. Ezhilarasan. "Spoof Detection of Fingerprint Biometrics based on Local and Global Quality Measures." International Journal of Computer Applications 124, no. 16 (August 18, 2015): 22–25. http://dx.doi.org/10.5120/ijca2015905804.

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Lee, Soon-Gyu, Jin-Woo Kim, Kyo-Won Ku, Hee-Chul Hwang, Seung-Hyun Moon, and Won-Jun Choe. "42‐5: Spoof Detection Scheme for Optical Fingerprint Sensors under the Display." SID Symposium Digest of Technical Papers 51, no. 1 (August 2020): 619–21. http://dx.doi.org/10.1002/sdtp.13944.

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Kumar, Pradeep, Rajkumar Saini, Barjinder Kaur, Partha Pratim Roy, and Erik Scheme. "Fusion of Neuro-Signals and Dynamic Signatures for Person Authentication." Sensors 19, no. 21 (October 28, 2019): 4641. http://dx.doi.org/10.3390/s19214641.

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Many biometric systems based on physiological traits such as ones facial characteristics, iris, and fingerprint have been developed for authentication purposes. Such security systems, however, commonly suffer from impersonation attacks such as obfuscation, abrasion, latent samples, and covert attack. More conventional behavioral methods, such as passwords and signatures, suffer from similar issues and can easily be spoofed. With growing levels of private data readily available across the internet, a more robust authentication system is needed for use in emerging technologies and mobile applications. In this paper, we present a novel multimodal biometric user authentication framework by combining the behavioral dynamic signature with the the physiological electroencephalograph (EEG) to restrict unauthorized access. EEG signals of 33 genuine users were collected while signing on their mobile phones. The recorded sequences were modeled using a bidirectional long short-term memory neural network (BLSTM-NN) based sequential classifier to accomplish person identification and verification. An accuracy of 98.78% was obtained for identification using decision fusion of dynamic signatures and EEG signals. The robustness of the framework was also tested against 1650 impersonation attempts made by 25 forged users by imitating the dynamic signatures of genuine users. Verification performance was measured using detection error tradeoff (DET) curves and half total error rate (HTER) security matrices using true positive rate (TPR) and false acceptance rate (FAR), resulting in 3.75% FAR and 1.87% HTER with 100% TPR for forgery attempts.

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Bhardwaj, Ishan, Narendra D. Londhe, and Sunil K. Kopparapu. "A spoof resistant multibiometric system based on the physiological and behavioral characteristics of fingerprint." Pattern Recognition 62 (February 2017): 214–24. http://dx.doi.org/10.1016/j.patcog.2016.09.003.

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Chatterjee, Amit, Vimal Bhatia, and Shashi Prakash. "Anti-spoof touchless 3D fingerprint recognition system using single shot fringe projection and biospeckle analysis." Optics and Lasers in Engineering 95 (August 2017): 1–7. http://dx.doi.org/10.1016/j.optlaseng.2017.03.007.

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Agarwal, Shivang, and C. Ravindranath Chowdary. "A-Stacking and A-Bagging: Adaptive versions of ensemble learning algorithms for spoof fingerprint detection." Expert Systems with Applications 146 (May 2020): 113160. http://dx.doi.org/10.1016/j.eswa.2019.113160.

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S. Raju, A., and V. Udayashankara. "A Survey on Unimodal, Multimodal Biometrics and Its Fusion Techniques." International Journal of Engineering & Technology 7, no. 4.36 (December 9, 2018): 689. http://dx.doi.org/10.14419/ijet.v7i4.36.24224.

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Presently, a variety of biometric modalities are applied to perform human identification or user verification. Unimodal biometric systems (UBS) is a technique which guarantees authentication information by processing distinctive characteristic sequences and these are fetched out from individuals. However, the performance of unimodal biometric systems restricted in terms of susceptibility to spoof attacks, non-universality, large intra-user variations, and noise in sensed data. The Multimodal biometric systems defeat various limitations of unimodal biometric systems as the sources of different biometrics typically compensate for the inherent limitations of one another. The objective of this article is to analyze various methods of information fusion for biometrics, and summarize, to conclude with direction on future research proficiency in a multimodal biometric system using ECG, Fingerprint and Face features. This paper is furnished as a ready reckoner for those researchers, who wish to persue their work in the area of biometrics.

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Nasiri-Avanaki, Mohammad-Reza, Alexander Meadway, Adrian Bradu, Rohollah Mazrae Khoshki, Ali Hojjatoleslami, and Adrian Gh Podoleanu. "Anti-Spoof Reliable Biometry of Fingerprints Using <i>En-Face</i> Optical Coherence Tomography." Optics and Photonics Journal 01, no. 03 (2011): 91–96. http://dx.doi.org/10.4236/opj.2011.13015.

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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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"A Robust Hybrid Biometric Face Recognition Payment System." International Journal of Recent Technology and Engineering 8, no. 6 (March 30, 2020): 5586–91. http://dx.doi.org/10.35940/ijrte.f9771.038620.

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Modern civilization has always been endeavoring to achieve a cashless and digital society. The emergence of payment methods like cards, net banking, and digital wallets have enabled the possibility of cashless and cardless online and offline payments. However, these payment methods are at the risk of theft and sometimes may require users to memorize different passwords. Biometric Payments may seem like a viable option but, the fingerprints can be spoofed and dirt particles may damage the fragile sensors. Face recognition payments are more frictionless than the present card, mobile and biometric payment systems as they do not require a device to carry out the transaction. It is also reliable, secure and efficient. Hence, saving time for both the customer and retailer. The previous system used Eigenfaces and Euclidean Distance for face recognition payment. Our proposed system uses Haar Cascades for face detection and Local Binary Patterns Histogram(LBPH) for face recognition. Our proposed approach is more efficient with respect to parameters such as noise reduction, threshold, training time, confidence and accuracy as it achieves a higher noise reduction and accuracy with a lower threshold, training time and confidence.

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"Contactless Fingerprint Recognition and Fingerprint Spoof Mitigation using CNN." International Journal of Recent Technology and Engineering 8, no. 4 (November 30, 2019): 9271–75. http://dx.doi.org/10.35940/ijrte.d9420.118419.

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Contactless identification of fingerprints has gained considerable attention as it can offer more hygienic and accurate personal identification. Despite these benefits, contactless 2D imagery often leads to partial 2D fingerprints as it requires relatively higher user cooperation during contactless 2D imagery. This paper develops a CNN framework for recognizing contactless fingerprint images–based on database. Our framework uses fingerprint minutiae and particular ridge map region to train a CNN first. Over several popular deep learning, our experiments presented in this paper achieve good results with greater accuracy. Experimental results obtained in this paper shows the effectiveness of the proposed approach and illustrate a significant improvement in methods of fingerprint recognition. The proposed work also helps to mitigate spoofing of fingerprints, thus providing greater security.

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"Fingerprint Detection Technique Using SURF, PHOG & PCA Feature Extraction Process." International Journal of Recent Technology and Engineering 8, no. 3 (September 30, 2019): 7266–69. http://dx.doi.org/10.35940/ijrte.c6341.098319.

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The biometric way of identifying a person are wildly spared around many industries and organizations. The identification techniques followed for the biometric are mostly common in using fingerprint detection for individual identification. Basically password based security systems are cracked through many techniques, which makes many problem to the organization using password for security purpose. The spoof fingerprint way of identifying a person is becoming very famous in providing security to the users. The research work focuses on proposing a novel approach in merging fingerprint features all together in one static software approach. The features identified from the fingerprints are extracted using histogram equations in initial step of fingerprint security system. The Gabor wavelet transformation techniques is one of the images processing technique used for identifying features. The features are maintained carefully with applying dynamic score level integration. The efficiency of proposed work is checked with LivDet 2011 dataset. The rate of classification shows 9.625% and error rate is 2.27%.

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Kanade, Harshada, Gauri Uttarwar, Shweta Borse, and Archana K. "Fingerprint Distortion Detection." International Journal of Scientific Research in Computer Science, Engineering and Information Technology, July 25, 2020, 559–62. http://dx.doi.org/10.32628/cseit2063204.

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Fingerprint is widely used in biometrics, for identification of individual’s identity. Biometric recognition is a leading technology for identification and security systems. It has unique identification among all other biometric modalities. Most anomaly detection systems rely upon machine learning. Calculations are performed to identify suspicious occasion. The primary purpose of this system is to ensure a reliable and accurate user authentication; this study addresses the problem of developing accurate, generalizable, and efficient algorithms for detecting fingerprint spoof attacks. The approach is to utilize local patches centered and aligned using fingerprint details. That proposed approach is to provide accuracies in fingerprint spoof detection for intra-sensor, cross material, crosssensor, as well as cross-dataset testing scenarios. The principle used is similar to the working of some cryptographic primitives, in particular to present the key into the plan so that a couple of operations are infeasible without knowing it.

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"Silicone fingers spoof fingerprint system in Brazil." Biometric Technology Today 2013, no. 4 (April 2013): 3. http://dx.doi.org/10.1016/s0969-4765(13)70067-5.

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Kumar, Ajay. "Fingerprint spoof detection using blood-flow analysis." SPIE Newsroom, 2009. http://dx.doi.org/10.1117/2.1200909.1794.

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Larin, Kirill. "Enhanced spoof proofing of fingerprint readers by optical coherence tomography." SPIE Newsroom, 2007. http://dx.doi.org/10.1117/2.1200701.0596.

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Ali, Syed Farooq, Muhammad Aamir Khan, and Ahmed Sohail Aslam. "Fingerprint matching, spoof and liveness detection: classification and literature review." Frontiers of Computer Science 15, no. 1 (September 29, 2020). http://dx.doi.org/10.1007/s11704-020-9236-4.

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Verma, Akhilesh, Vijay Kumar Gupta, and Savita Goel. "Fingerprint Presentation Attack Detection in Open-Set Scenario using Transient Liveness Factor." Recent Advances in Computer Science and Communications 13 (April 23, 2020). http://dx.doi.org/10.2174/2666255813999200423123033.

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Background: In recent history, fingerprint presentation attack detection (FPAD) proposal came out in a variety of ways. A close-set approach uses pattern classification technique that best suits to a specific context and goal. Openset approach works fine in wider context, which is relatively robust with new fabrication material and independent of sensor type. In both case results were promising but not too generalizable because of unseen condition not fitting into method used. It is clear, the two key challenges in FPAD system, sensor interoperability and robustness with new fabrication materials not addressed to date. Objective: To address above challenge a liveness detection model is proposed using live sample using transient liveness factor and one-class CNN. Methods: In our architecture, liveness is predicted by using the fusion rule, score level fusion of two decisions. Here, ‘n’ high quality live samples are initially trained for quality. We have observed that fingerprint liveness information is ‘transitory’ in nature, a variation in the different live sample is natural. Thus, each live sample has a ‘transient liveness’ (TL) information. We use no-reference (NR) image quality measure (IQM) as a transient value corresponding to each live sample. A consensus agreement is collectively reached in transient value to predict adversarial input. Further, live sample at server are trained with augmented inputs on the one-class classifier to predict the outlier. So, by using the fusion rule, score level fusion of consensus agreement and appropriately characterized negative cases (or outliers) predicts liveness. Results: Our approach uses high quality 30-live sample only, out of 90 images available in dataset to reduce learning time. We used Time Series images from LivDet competition 2015. It has 90-live images and 45-spoof images made from Bodydouble, Ecoflex and Playdoh of each person. Fusion rule results in 100% accuracy in recognising live as live. Conclusion: We have presented an architecture for liveness-server for extraction/updating transient liveness factor. Our work explained here a significant step forward towards generalized and reproducible process with a consideration towards the provision for the universal scheme as a need of today. The proposed TLF approach has a solid presumption; it will address dataset heterogeneity as it incorporates wider scope-context. Similar results with other dataset are under validation. Implementation seems difficult now but have several advantages when carried out during the transformative process.

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Song, Hyoung-Kyu, Ebrahim AlAlkeem, Jaewoong Yun, Tae-Ho Kim, Hyerin Yoo, Dasom Heo, Myungsu Chae, and Chan Yeob Yeun. "Deep user identification model with multiple biometric data." BMC Bioinformatics 21, no. 1 (July 16, 2020). http://dx.doi.org/10.1186/s12859-020-03613-3.

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Abstract Background Recognition is an essential function of human beings. Humans easily recognize a person using various inputs such as voice, face, or gesture. In this study, we mainly focus on DL model with multi-modality which has many benefits including noise reduction. We used ResNet-50 for extracting features from dataset with 2D data. Results This study proposes a novel multimodal and multitask model, which can both identify human ID and classify the gender in single step. At the feature level, the extracted features are concatenated as the input for the identification module. Additionally, in our model design, we can change the number of modalities used in a single model. To demonstrate our model, we generate 58 virtual subjects with public ECG, face and fingerprint dataset. Through the test with noisy input, using multimodal is more robust and better than using single modality. Conclusions This paper presents an end-to-end approach for multimodal and multitask learning. The proposed model shows robustness on the spoof attack, which can be significant for bio-authentication device. Through results in this study, we suggest a new perspective for human identification task, which performs better than in previous approaches.

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