Security and Accuracy of Fingerprint-Based Biometrics: A Review <s> Attacks to User Interface and Countermeasures <s> Although biometric information is known to be unique and unreproducible, a recent research reported that certain fingerprint sensor modules can be spoofed by artificial fingers. This study extends the previous research by testing more various fingerprint sensors and evaluating their capability of liveness detection in a quantitative way. The purpose of this research is to propose a framework of performance evaluation, evaluation indices, and reporting of evaluation results for fingerprint sensor modules. A preliminary experiment tested four commercial fingerprint sensor modules each of which is a representative of a different sensing mechanism such as optical, semiconductor, thermal, or tactile against two artificial fingerprints, gummy fingers and silicon-rubber fingers. The results of this research can be utilized to improve the methods of the liveness detection of fingerprint recognition systems. <s> BIB001 </s> Security and Accuracy of Fingerprint-Based Biometrics: A Review <s> Attacks to User Interface and Countermeasures <s> Fingerprint scanners can be spoofed by artificial fingers using moldable plastic, clay, Play-Doh, gelatin, silicone rubber materials, etc. Liveness detection is an anti-spoofing method which can detect physiological signs of life from fingerprints to ensure only live fingers can be captured for enrollment or authentication. In this paper, a new method based on the wavelet transform on the ridge signal extracted along the ridge mask is proposed which can detect the perspiration phenomenon using only a single image. Statistical features are extracted for multiresolution scales to discriminate between live and non-live fingers. Based on these features, we use a classification tree to generate the decision rules for the liveness classification. We test this method on the dataset which contains about 58 live, 80 spoof (50 made from Play-Doh and 30 made from gelatin), and 25 cadaver subjects for 3 different scanners. Also, we test this method on a second dataset which contains 33 live and 33 spoof (made from gelatin) subjects. The proposed liveness detection method is purely software based and application of this method can provide anti-spoofing protection for fingerprint scanners. <s> BIB002 </s> Security and Accuracy of Fingerprint-Based Biometrics: A Review <s> Attacks to User Interface and Countermeasures <s> The automatic vitality detection of a fingerprint has become an important issue in personal verification systems based on this biometric. It has been shown that fake fingerprints made using materials like gelatine or silicon can deceive commonly used sensors. Recently, the extraction of vitality features from fingerprint images has been proposed to address this problem. Among others, static and dynamic features have been separately studied so far, thus their respective merits are not yet clear; especially because reported results were often obtained with different sensors and using small data sets which could have obscured relative merits, due to the potential small sample-size issues. In this paper, we compare some static and dynamic features by experiments on a larger data set and using the same optical sensor for the extraction of both feature sets. We dealt with fingerprint stamps made using liquid silicon rubber. Reported results show the relative merits of static and dynamic features and the performance improvement achievable by using such features together. <s> BIB003 </s> Security and Accuracy of Fingerprint-Based Biometrics: A Review <s> Attacks to User Interface and Countermeasures <s> A new software-based liveness detection approach using a novel fingerprint parameterization based on quality related features is proposed. The system is tested on a highly challenging database comprising over 10,500 real and fake images acquired with five sensors of different technologies and covering a wide range of direct attack scenarios in terms of materials and procedures followed to generate the gummy fingers. The proposed solution proves to be robust to the multi-scenario dataset, and presents an overall rate of 90% correctly classified samples. Furthermore, the liveness detection method presented has the added advantage over previously studied techniques of needing just one image from a finger to decide whether it is real or fake. This last characteristic provides the method with very valuable features as it makes it less intrusive, more user friendly, faster and reduces its implementation costs. <s> BIB004 </s> Security and Accuracy of Fingerprint-Based Biometrics: A Review <s> Attacks to User Interface and Countermeasures <s> Apple claims that iPhone 6, which is equipped with iOS 8.0 and later version, is secure enough to prevent a user's private data from law enforcement or malicious intruders. In pre-iOS 8.0 operating systems, a user's data were only encrypted by hardware-based keys, which can be obtained by Apple. But in iOS 8.0 and later version, the private data on the iPhone are protected by a secret key that is protected by the user's passcode, which the Apple does not hold. In this paper, supported by real-life experiments, we demonstrate that several vulnerabilities of iPhone 6 with iOS 8, which are brought by ordinary user operations, can lead to the leakage of the private data. Then we conduct vulnerability analysis and give the reasons that cause these vulnerabilities from a technical perspective. Meanwhile, experiments of forging attack aiming at iPhone 6 Touch ID are conducted. <s> BIB005 </s> Security and Accuracy of Fingerprint-Based Biometrics: A Review <s> Attacks to User Interface and Countermeasures <s> In this letter, we propose a novel image descriptor for fingerprint liveness detection using the local coherence of a given image. Based on the observation that materials employed for making fake fingerprints (e.g., silicone, wood glue, etc.) tend to yield the nonuniformity in the captured image due to the replica fabrication process, we focus on the difference of the dispersion in the image gradient field between live and fake fingerprints. More specifically, we propose to define the local patterns of the coherence along the dominant direction, the so-called local coherence patterns, as our features, which are fed into the linear support vector machine (SVM) classifier to determine whether a given fingerprint is fake or not. Experimental results on various datasets show that the proposed image descriptor is effective for fingerprint liveness detection compared to other approaches employed in the literature. <s> BIB006 </s> Security and Accuracy of Fingerprint-Based Biometrics: A Review <s> Attacks to User Interface and Countermeasures <s> Fingerprint-based authentication systems need to be secured against spoof attacks. In this paper, we propose completed local binary pattern (CLBP) texture descriptor with wavelet transform (WT) for fingerprint liveness detection. The fundamental basis of the proposed method is live, and spoof finger images differ in textural characteristics due to gray-level variations. These textural characteristics occur at various scales and orientations. CLBP has high discriminatory power as it takes into account local sign and magnitude difference with average gray level of an image. CLBP extended to 2-D Discrete WT (DWT), and 2-D Real Oriented Dual Tree WT (RODTWT) domain captures texture features at multiple scales and orientations. Each image was decomposed up to four levels, and CLBP features computed at each level are classified using linear and RBF kernel support vector machine (SVM) classifiers. Extensive comparisons are made to evaluate influence of wavelet decomposition level, wavelet type, number of wavelet orientations, and feature normalization method on fingerprint classification performance. CLBP in WT domain has proved to offer effective classification performance with simplicity of computation. While texture features at each scale contribute to performance, higher performance is achieved at lower decomposition levels of high resolution with db2 and db1 wavelets, RBF SVM and mean normalized features. <s> BIB007 </s> Security and Accuracy of Fingerprint-Based Biometrics: A Review <s> Attacks to User Interface and Countermeasures <s> With increasing markets for fingerprint authentication, there are also increasing concerns about spoofs or synthetically produced fingerprint identifications that can bypass the authentication process. In this Letter, the authors introduce a new convolutional neural networks (CNNs) architecture for fingerprint liveness detection problem that can provide a more robust framework for network training and detection than previous methods. The proposed method employs squared regression error for each receptive field without the usage of the fully connected layer. Such structure provides following advantages from the previous liveness fingerprint CNN. First, unlike the previous techniques which rely on the pre-trained features, the proposed CNN can be trained directly from fingerprints as the loss is minimised for each receptive field. Second, in contrast to the cross-entropy layer, the squared error layer allows them to set up a threshold value that can control the acceptable level of false positives or false negatives. Third, the absence of a fully connected layer allows them to crop the input fingerprints such that a trade-off between accuracy and computation time can be made without the negative effects of re-scaling. The proposed CNN is shown to provide higher accuracy for three out of four datasets when evaluated against the state-of-the-art method. <s> BIB008 </s> Security and Accuracy of Fingerprint-Based Biometrics: A Review <s> Attacks to User Interface and Countermeasures <s> With the growing use of fingerprint identification systems in recent years, preventing fingerprint identification systems from being spoofed by artificial fake fingerprints has become a critical problem. In this paper, we put forward a novel method to detect fingerprint liveness based on BP neural network, which is used for the first time in the fingerprint liveness detection. Moreover, different from traditional detection methods, we propose a scheme to construct the input data and corresponding category labels. More effective and efficient texture features of fingerprints, which are used as the input data of the BP neural network, are computed to improve classification performance and obtain a better pre-trained network model. After a variety of preprocessing operations and image compression operations, gradient values in the horizontal and vertical directions are computed by using Laplacian operator, and difference co-occurrence matrices are constructed from the obtained gradient values. Then, the input data of neural network model are built based on two DCMs. The pre-trained neural network models with diverse neuron nodes are learnt. Different experiments based on different parameters for the BP neural network have been conducted. Finally, classification accuracy of testing fingerprints is predicted based on the pre-trained networks. Experimental results on the LivDet 2013 show that the classification performance of our proposed method is effective and meanwhile provides a better detection accuracy compared with the majority of previously published results. <s> BIB009 </s> Security and Accuracy of Fingerprint-Based Biometrics: A Review <s> Attacks to User Interface and Countermeasures <s> In recent years, fingerprint authentication systems have been extensively deployed in various applications, including attendance systems, authentications on smartphones, mobile payment authorizations, as well as various safety certifications. However, similar to the other biometric identification technologies, fingerprint recognition is vulnerable to artificial replicas made from cheap materials, such as silicon, gelatin, etc. Thus, it is especially necessary to distinguish whether a given fingerprint is a live or a spoof one prior to such authentication. In order to solve the problems above, a novel local descriptor named Weber local binary descriptor for fingerprint liveness detection (FLD) has been proposed in this paper. The method consists of two components: the local binary differential excitation component that extracts intensity-variance features and the local binary gradient orientation component that extracts orientation features. The co-occurrence probability of the two components is calculated to construct a discriminative feature vector, which is fed into support vector machine (SVM) classifiers. The effectiveness of the proposed method is intuitively analyzed on the image samples and numerically demonstrated by Mahalanobis distance. Experiments are performed on two public databases from FLD competitions from 2011 and 2013. The results have proved that the proposed method obtains the best detection accuracy among the existing image local descriptors in FLD. <s> BIB010
Spoofing attacks to the user interface (the sensor module) are mostly because of the presentation of a fake biometric trait. Since biometric traits are not secret, an adversary can intrude into the system with a fake trait (e.g., artificial fingerprint, face mask) to spoof the biometric system if the system is unable to differentiate between a fake and a genuine biometric trait. A number of fingerprint sensors are tested to see if they can reject a fake fingerprint film. The test results show that the fake finger films are accepted by most of the tested sensors BIB001 . Also, a total of 11 different fingerprint-based authentication systems are attacked with fake fingerprint films  , with results showing that fake fingerprint films can be enrolled in the systems and fake fingerprints are accepted with more than 67% probability. With the ever-increasing popularity of iPhones, great attention has been drawn to the fingerprint spoofing attack to Touch ID. For example, a latent fingerprint was lifted from the iPhone screen. From the lifted latent fingerprint, a mold was created by using a printed circuit board (PCB) by a researcher from Chaos Computer Club (CCC). By filling the art glue into the mold on the PCB BIB005 , he subsequently generated a rubber fingerprint film, by which Touch ID of the iPhone can be fooled. Liveness detection is an effective countermeasure to fake biometric attacks. In recent years, strenuous work has been done in the research of liveness detection, which is used to detect whether the presented feature is from a live human being or not. Two major schemes are available to implement liveness detection. One scheme constitutes software-based solutions, which utilize the information already captured by biometric sensors, while the other scheme includes hardware-based solutions  . However, hardware-based solutions are usually more expensive. Tan and Schuckers BIB002 presented a wavelet transform based method to detect the perspiration phenomenon, so as to tell difference between live and non-live fingers. The perspiration phenomenon can be quantified by using the statistical features, which represent the gray level values along the ridge mask in an image. Experimental results demonstrate that with the proposed method, optical scanners are able to detect live fingers. To prevent spoof attacks from gelatin or silicon fake-fingerprints from deceiving some commonly used fingerprint sensors, Coli et al. BIB003 utilized static features together with dynamic features for fingerprint vitality detection. Before this method was proposed, the static and dynamic features of a fingerprint had been studied separately. Relevant benefits of using both features and performance improvement were achieved and reported in the paper. Galbally et al. BIB004 proposed an approach using fingerprint parameterization based on quality related features for liveness detection. The liveness detection process can be considered as a two-class, real or fake, classification problem. The key point of this problem is to find and use a set of unique patterns to generate a classifier that outputs the probability of a fingerprint image. The proposed approach is able to perform classification based on the single acquired sample rather than multiple different samples of the fingerprint, which makes the acquisition process of a sample faster and more expedient than existing methods. The proposed approach was tested on several publicly available databases and good accuracy was reported (e.g., almost 9 out of 10 of the fingerprint images were classified correctly). Kim BIB006 designed an image descriptor to handle fingerprint liveness detection. It is observed BIB006 that fake fingerprints tend to generate non-uniformity in the captured image for the replica fabrication process, so the difference of the dispersion in the image gradient field is exploited to distinguish live and fake fingerprints. In the proposed method, a new feature called local coherence pattern is defined, which is a local pattern of coherence along the dominant direction. After the proposed feature set is fed into the support vector machine (SVM), a decision on a real or fake fingerprint can be made. Jung and Heo BIB008 introduced a convolutional neural network (CNN) architecture to deal with the liveness detection issue. The proposed architecture is a robust framework for training and detection. Squared regression error for each receptive field is employed in this architecture and the training can be performed directly from each fingerprint. System performance is controlled by a threshold value in the squared error layer. Kundargi and Karandikar BIB007 proposed using a texture descriptor, called completed local binary pattern (CLBP), together with the wavelet transform (WT) for fingerprint liveness detection. By considering the local sign and magnitude difference with the average gray level of a fingerprint image, the CLBP possesses high discriminatory power. Experimental results verified that the CLBP in the WT domain can offer satisfactory classification performance. Xia et al. BIB010 developed a local descriptor, namely, Weber local binary, for fingerprint liveness detection. The proposed method is composed of two modules, namely local binary differential excitation module and local binary gradient orientation module. The outputs of these two modules form a discriminative feature vector that is input into the SVM classifiers. Yuan et al. BIB009 introduced a BP neural network based fingerprint liveness detection method. In this method, image gradient values are obtained by the Laplacian operator and different parameters for the BP neural network are tested to achieve better detection accuracy. In this section, as the countermeasure to spoofing attacks, several liveness detection methods are reviewed. Non-machine learning based algorithms BIB002 BIB003 BIB004 BIB006 BIB007 and machine learning based algorithms BIB008 BIB010 BIB009 were proposed to extract unique features to ascertain whether an input fingerprint is fake or real. The three machine learning based algorithms BIB008 BIB010 BIB009 were all recently published (in 2018), which shows that machine learning is playing an active role in liveness detection design. A comparison of all the above approaches to fingerprint liveness detection is reported in Table 2 . 