Fingerprint Liveness Detection Using Gray Level Co-Occurrence Matrix Based Texture Feature

Yuan, Chengsheng; Xia, Zhihua; Sun, Xingming; Sun, Deqing; Li, Rui

doi:10.14257/ijgdc.2016.9.10.06

Cited by 4 publications

(4 citation statements)

References 21 publications

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“…To evaluate the detection performance of our proposed algorithm, our experimental results are compared with several state-of-the-art approaches, including ULBP [16], HOG [33], MSDCM [9], Winner LCP [29], LLF and HIGMC [30]. Tables 4 and 5 respectively list the newly proposed algorithms on the two data sets LivDet 2013 and LivDet 2011, and we can observe that the ACEs of our method are 1.57 and 0.185 lower than the second place respectively, which are highlighted in bold.…”

Section: Experimental Process and Resultsmentioning

confidence: 99%

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Fingerprint Liveness Detection Using an Improved CNN With Image Scale Equalization

Yuan¹,

Xia²,

Jiang³

et al. 2019

IEEE Access

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Due to the lack of pre-judgment of fingerprints, fingerprint authentication systems are frequently vulnerable to artificial replicas. Anonymous people can impersonate authorized users to complete various authentication operations, thereby disrupting the order of life and causing tremendous economic losses to society. Therefore, to ensure that authorized users' fingerprint information is not used illegally, one possible anti-spoofing technique, called fingerprint liveness detection (FLD), has been exploited. Compared with the hand-crafted feature methods, the deep convolutional neural network (DCNN) can automatically learn the high-level semantic detail via supervised learning algorithm without any professional background knowledge. However, one disadvantage of most CNNs models is that fixed scale images (e.g., 227 × 227) are essential in the input layer. Although the scale problem can be handled by cropping or scaling operations via transforming an image of any scale into a fixed scale, they can easily cause some key texture information loss and image resolution degradation, which will weaken the generalization performance of the classifier model. In this paper, a novel FLD method called an improved DCNN with image scale equalization, has been proposed to preserve texture information and maintain image resolution. Besides, an adaptive learning rate method has been used in this paper. In the performance evaluation, the confusion matrix is applied into FLD for the first time as a performance indicator. The amounts of the experimental results based on the LivDet 2011 and LivDet 2013 data sets also verify that the detection performance of our method is superior to other methods.

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Section: Experimental Process and Resultsmentioning

confidence: 99%

“…Finally, images of any size can be expressed in fixed length vectors without any cropping or scaling operations. Moreover, our method is also robust to fingerprint image deformation without scaling operation [27], [32], [33]. The flowchart of this paper is shown in Figure 3.…”

Section: Introductionmentioning

confidence: 99%

Fingerprint Liveness Detection Using an Improved CNN With Image Scale Equalization

Yuan¹,

Xia²,

Jiang³

et al. 2019

IEEE Access

Self Cite

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“…In feature-based image-matching methods, feature extraction is a very important component. Traditional feature extraction methods mainly include scale-invariant feature transform (SIFT) [18], oriented fast and rotated brief (ORB) [19], features from accelerated segment test (FAST) [20], histogram of orientated gradient (HOG) [21], affine-SIFT (ASIFT) [22], binary robust invariant scalable keypoints (BRISK), binary fisheye spherical distorted robust independent elemental features (FSD-BRIEF) [23], etc. Because traditional feature extraction methods do not fully utilize data, they can only extract certain aspects of image features.…”

Section: Introductionmentioning

confidence: 99%

Multi-Modal Scene Matching Location Algorithm Based on M2Det

Fan,

Yang,

et al. 2023

CMC

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In recent years, many visual positioning algorithms have been proposed based on computer vision and they have achieved good results. However, these algorithms have a single function, cannot perceive the environment, and have poor versatility, and there is a certain mismatch phenomenon, which affects the positioning accuracy. Therefore, this paper proposes a location algorithm that combines a target recognition algorithm with a depth feature matching algorithm to solve the problem of unmanned aerial vehicle (UAV) environment perception and multi-modal image-matching fusion location. This algorithm was based on the single-shot object detector based on multi-level feature pyramid network (M2Det) algorithm and replaced the original visual geometry group (VGG) feature extraction network with the ResNet-101 network to improve the feature extraction capability of the network model. By introducing a depth feature matching algorithm, the algorithm shares neural network weights and realizes the design of UAV target recognition and a multi-modal image-matching fusion positioning algorithm. When the reference image and the real-time image were mismatched, the dynamic adaptive proportional constraint and the random sample consensus consistency algorithm (DAPC-RANSAC) were used to optimize the matching results to improve the correct matching efficiency of the target. Using the multi-modal registration data set, the proposed algorithm was compared and analyzed to verify its superiority and feasibility. The results show that the algorithm proposed in this paper can effectively deal with the matching between multi-modal images (visible image-infrared image, infrared image-satellite image, visible image-satellite image), and the contrast, scale, brightness, ambiguity deformation, and other changes had good stability and robustness. Finally, the effectiveness and practicability of the algorithm proposed in this paper were verified in an aerial test scene of an S1000 sixrotor UAV.

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“…However, as a result of overuse, fingerprints are becoming the targets of attackers or imposters. Scholars [4] have proven that intelligent devices with fingerprint identification are vulnerable to artificial replicas made from common materials, such as silica, gelatin, clay, and Play-Doh, and attackers or imposters can hinder these optical and capacitive sensors using these forged fingerprints when fingers press on the surface of the scanners. Thus, one of the common problems with these intelligent devices is that they cannot guarantee the authenticity of fingerprints before identification; specifically, they cannot distinguish between genuine or fake fingerprints [5].…”

Section: Introductionmentioning

confidence: 99%

Fingerprint Liveness Detection Based on Fine-Grained Feature Fusion for Intelligent Devices

Cheng

Yuan

et al. 2020

Mathematics

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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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Fingerprint Liveness Detection Using Gray Level Co-Occurrence Matrix Based Texture Feature

Abstract: Fingerprint-based recognition systems have been widely deployed in numerous civilian

Cited by 4 publications

References 21 publications

Fingerprint Liveness Detection Using an Improved CNN With Image Scale Equalization

Fingerprint Liveness Detection Using an Improved CNN With Image Scale Equalization

Multi-Modal Scene Matching Location Algorithm Based on M2Det

Fingerprint Liveness Detection Based on Fine-Grained Feature Fusion for Intelligent Devices

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