• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.5
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• pp.1259-1267
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• 2016

With the growing use of fingerprint authentication systems in recent years, the fake fingerprint detection is becoming more and more important. This paper mainly proposes a method for fake fingerprint detection based on CNN, it will visualize the distinctive part of detected fingerprint which provides a deeper insight in CNN model. After the preprocessing part using fingerprint segmentation, the pretrained CNN model is used for detecting the liveness detection. Not only a liveness detection but also feature analysis about the live fingerprint and fake fingerprint are provided after classifying which materials are used for making the fake fingerprint. Our system is evaluated on three databases in LivDet2013, which compromise almost 6500 live fingerprint images and 6000 fake fingerprint images in total. The proposed method achieves 3.1% ACE value about the liveness detection and achieves 79.58% accuracy on LiveDet2013.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.1
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• pp.39-47
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• 2017

Nowadays, there have been an increasing number of illegal use cases where people try to fabricate the working hours by using fake fingerprints. So, the fingerprint liveness detection techniques have been actively studied and widely demanded in various applications. This paper proposes a new method to detect fake fingerprints using CNN (Convolutional Neural Ntworks) based on the patches of fingerprint images. Fingerprint image is divided into small square sized patches and each patch is classified as live, fake, or background by the CNN. Finally, the fingerprint image is classified into either live or fake based on the voting result between the numbers of fake and live patches. The proposed method does not need preprocessing steps such as segmentation because it includes the background class in the patch classification. This method shows promising results of 3.06% average classification errors on LivDet2011, LivDet2013 and LivDet2015 dataset.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.2
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• pp.71-75
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• 2017

In this paper, a smart optical fingerprint sensor technology that is robust against faked fingerprints. A new lens and prism accurately detect fingerprint ridges and valleys that are needed to express a fingerprint's intrinsic characteristics well. The proposed technology includes light path configuration and an optical fingerprint sensor that can effectively identify faked fingerprint features. Results of simulation show the smart optical fingerprint sensor classifies the characteristics of faked fingerprints made from silicone, gelatin, paper, and rubber, and show that the proposed technology has superior detection performance with faked fingerprints, compared to the existing infrared discrimination method.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.295-296
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• 2007

Fake fingersubmission to the sensor is a major problem in fingerprint recognition systems. In this paper, we introduce a novel liveness detection method using multi-static features. For convenience and usefulness of field application, static features are only considered to detect 'live' and 'fake' fingerprint images. Individual pore spacing, noise of image and first order statistics of image are analyzed as our static features to reflect the Physiological and statistical characteristics of live and fake fingerprint.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.2
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• pp.305-314
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• 2017

Recently, user authentication through biometric traits such as fingerprint and iris raise more and more attention especially in mobile commerce and fin-tech fields. In particular, commercialized authentication methods using fingerprint recognition are widely utilized mainly because customers are more adopted and used to fingerprint recognition applications. In the meantime, the security issues caused by fingerprint falsification bring lots of attention. In this paper, we propose a new method to improve the performance of fake fingerprint detection using CNN(Convolutional Neural Network). It is common practice to increase the amount of learning data by using affine transformation or horizontal reflection to improve the detection rate in CNN characteristics that are influenced by learning data. However, in this paper we propose an effective data augmentation method based on the database difficulty level. The experimental results confirm the validity of proposed method.

• Journal of Korea Multimedia Society
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• v.11 no.4
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• pp.492-503
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• 2008

With the recent development and increasing importance of personal identification systems, biometric technologies with less risk of loss or unauthorized use are being popularized rapidly. In particular, because of their high identification rate and convenience, fingerprint identification systems are being used much more commonly than other biometric systems such as iris recognition, face recognition and vein pattern recognition. However, a fingerprint identification system has the problem that artificially forged finger-prints can be used as input data. Thus, in order to solve this problem, the present study proposed a method for detecting forged fingerprints by measuring the degree of attenuation when the light from an optical fingerprint sensor passes through the finger and analyzing changes in the transmission of light over stages at fixed intervals. In order to prove improvement in the performance of the proposed system, we conducted an experiment that compared the system with an existing multi-sensor recognition system that measures also the temperature of fingerprint. According to the results of the experiment, the proposed system improved the forged fingerprint detection rate by around 32.6% and this suggests the possibility of solving the security problem in fingerprint identification systems.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 2006.06a
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• pp.241-245
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• 2006

Recent studies show that fingerprint recognition technology is confronted with spoofing of artificial fingers. In order to overcome this problem, the fingerprint recognition system needs to distinguish a fake finger from a live finger. This paper examines existing software-based approaches for fingerprint liveness detection through experiments. Implemented and tested in this paper are the approaches based on deformation, wavelet, and perspiration. These approaches will be analyzed and compared based on experimental results.

• Journal of information and communication convergence engineering
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• v.18 no.2
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• pp.132-146
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• 2020

Biometric technologies have become widely available in many different fields. However, biometric technologies using existing physical features such as fingerprints, facial features, irises, and veins must consider forgery and alterations targeting them through fraudulent physical characteristics such as fake fingerprints. Thus, a trend toward next-generation biometric technologies using behavioral biometrics of a living person, such as bio-signals and walking characteristics, has emerged. Accordingly, in this study, we developed a bio-signal authentication algorithm using electrocardiogram (ECG) signals, which are the most uniquely identifiable form of bio-signal available. When using ECG signals with our system, the personal identification and authentication accuracy are approximately 90% during a state of rest. When using fingerprints alone, the equal error rate (EER) is 0.243%; however, when fusing the scores of both the ECG signal and fingerprints, the EER decreases to 0.113% on average. In addition, as a function of detecting a presentation attack on a mobile phone, a method for rejecting a transaction when a fake fingerprint is applied was successfully implemented.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.892-911
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• 2019

Biometric recognition systems have been widely used for information security. Among the most popular biometric traits, there are fingerprint and face due to their high recognition accuracies. However, the security system that uses face recognition as the login method are vulnerable to face-spoofing attacks, from using printed photo or video of the valid user. In this study, we propose a fast and robust method to detect face-spoofing attacks based on the analysis of spatial frequency differences between the real and fake videos. We found that the effect of a spoofing attack stands out more prominently in certain regions of the 2D Fourier spectra and, therefore, it is adequate to use the information about those regions to classify the input video or image as real or fake. We adopt a divide-conquer-aggregate approach, where we first divide the frequency domain image into local blocks, classify each local block independently, and then aggregate all the classification results by the weighted-sum approach. The effectiveness of the methodology is demonstrated using two different publicly available databases, namely: 1) Replay Attack Database and 2) CASIA-Face Anti-Spoofing Database. Experimental results show that the proposed method provides state-of-the-art performance by processing fewer frames of each video.