To ensure the actual presence of a real legitimate trait in contrast to a fake self-manufactured synthetic or reconstructed sample is a significant problem in biometric authentication, which requires the development of new and efficient protection measures. In this paper, we present a novel software-based fake detection method that can be used in multiple biometric systems to detect different types of fraudulent access attempts. The objective of the proposed system is to enhance the security of biometric recognition frameworks, by adding liveness assessment in a fast, user-friendly, and non-intrusive manner, through the use of image quality assessment. The proposed approach presents a very low degree of complexity, which makes it suitable for real-time applications, using 25 general image quality features extracted from one image (i.e., the same acquired for authentication purposes) to distinguish between legitimate and impostor samples. The experimental results, obtained on publicly available data sets of fingerprint, iris, and 2D face, show that the proposed method is highly competitive compared with other state-of-the-art approaches and that the analysis of the general image quality of real biometric samples reveals highly valuable information that may be very efficiently used to discriminate them from fake traits.

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Image Quality Assessment for Fake Biometric Detection: Application to Iris, Fingerprint, and Face Recognition

With the growing use of biometric authentication systems in the recent years, spoof fingerprint detection has become increasingly important. In this paper, we use convolutional neural networks (CNNs) for fingerprint liveness detection. Our system is evaluated on the data sets used in the liveness detection competition of the years 2009, 2011, and 2013, which comprises almost 50 000 real and fake fingerprints images. We compare four different models: two CNNs pretrained on natural images and fine-tuned with the fingerprint images, CNN with random weights, and a classical local binary pattern approach. We show that pretrained CNNs can yield the state-of-the-art results with no need for architecture or hyperparameter selection. Data set augmentation is used to increase the classifiers performance, not only for deep architectures but also for shallow ones. We also report good accuracy on very small training sets (400 samples) using these large pretrained networks. Our best model achieves an overall rate of 97.1% of correctly classified samples—a relative improvement of 16% in test error when compared with the best previously published results. This model won the first prize in the fingerprint liveness detection competition 2015 with an overall accuracy of 95.5%.

Fingerprint Liveness Detection Using Convolutional Neural Networks

Several issues related to the vulnerability of fingerprint recognition systems to attacks have been highlighted in the biometrics literature. One such vulnerability involves the use of artificial fingers, where materials such as Play-Doh, silicone, and gelatin are inscribed with fingerprint ridges. Researchers have demonstrated that some commercial fingerprint recognition systems can be deceived when these artificial fingers are placed on the sensor; that is, the system successfully processes the ensuing fingerprint images, thereby allowing an adversary to spoof the fingerprints of another individual. However, at the same time, several countermeasures that discriminate between live fingerprints and spoof artifacts have been proposed. While some of these antispoofing schemes are hardware based, several software-based approaches have been proposed as well. In this article, we review the literature and present the state of the art in fingerprint antispoofing.

A Survey on Antispoofing Schemes for Fingerprint Recognition Systems

Off-line signature verification based on grey level information using texture features

Fingerprint authentication system is used to verify users' identification according to the characteristics of their fingerprints. However, this system has some security and privacy problems. For example, some artificial fingerprints can trick the fingerprint authentication system and access information using real users' identification. Therefore, a fingerprint liveness detection algorithm needs to be designed to prevent illegal users from accessing privacy information. In this paper, a new software-based liveness detection approach using multi-scale local phase quantity (LPQ) and principal component analysis (PCA) is proposed. The feature vectors of a fingerprint are constructed through multi-scale LPQ. PCA technology is also introduced to reduce the dimensionality of the feature vectors and gain more effective features. Finally, a training model is gained using support vector machine classifier, and the liveness of a fingerprint is detected on the basis of the training model. Experimental results demonstrate that our proposed method can detect the liveness of users' fingerprints and achieve high recognition accuracy. This study also confirms that multi-resolution analysis is a useful method for texture feature extraction during fingerprint liveness detection.

Fingerprint liveness detection based on multi-scale LPQ and PCA

This paper describes an image-based system to detect spoof fingerprint attacks in fingerprint biometric systems. It is based on the observation that, real and spoof fingerprints exhibit different textural characteristics. These are based on structural, orientation, roughness, smoothness and regularity differences of diverse regions in a fingerprint image. Local binary pattern (LBP) histograms are used to capture these textural details. Wavelet energy features characterizing ridge frequency and orientation information are also used for improving the efficiency of the proposed method. Dimensionality of the integrated feature set is reduced by running Pudilpsilas Sequential Forward Floating Selection (SFFS) algorithm. We propose to use a hybrid classifier, formed by fusing three classifiers: neural network, support vector machine and k-nearest neighbor using the ldquoProduct Rulerdquo. Classification rates achieved with these classifiers, including a hybrid classifier are in the range ~94% to ~97%. Experimental results indicate that, the new liveness detection approach is a very promising technique, as it needs only one fingerprint and no extra hardware to detect vitality.

Texture and Wavelet-Based Spoof Fingerprint Detection for Fingerprint Biometric Systems

This paper proposes a new curvelet transform-based method to detect spoof fingerprint attacks in fingerprint biometric systems. It uses only one image to differentiate a real fingerprint from a spoof one. It is based on the observation that, real and spoof fingerprints exhibit different textural characteristics. Textural measures based on curvelet energy signatures and curvelet co-occurrence signatures are used to characterize fingerprint texture. Dimensionalities of the feature sets are reduced by running pudil's sequential forward floating selection (SFFS) algorithm. We test two feature sets independently on various classifiers like: AdaBoost. M1, support vector machine and k-nearest neighbor; then we fuse all the mentioned classifiers using the "majority voting rule" to form an Ensemble classifier. Classification rates achieved with these classifiers for energy signatures range from ~94.12% to ~97.41%. Likewise, classification rates for co-occurrence signatures range from ~94.35% to ~98.12%. Thus, the performance of a new liveness detection approach is very promising, as it needs only one fingerprint and no extra hardware to detect vitality.

Fingerprint Liveness Detection Using Curvelet Energy and Co-Occurrence Signatures

Perspiration phenomenon is very significant to detect liveness of a finger. It requires two consecutive fingerprints to notice perspiration and therefore the method is slow. Some other methods in the literature need extra hardware to detect liveness. To alleviate these problems, in this paper, a new texture-based method which needs only one fingerprint is proposed. It is based on the observation that, real and spoof fingerprints exhibit different textural characteristics. Local binary pattern (LBP) histograms are used to capture these textural details. Wavelet energy features characterising ridge frequency and orientation information are also used for improving the efficiency of the proposed method. Dimensionalities of the feature sets are reduced by running sequential forward floating selection (SFFS). LBP features and wavelet energy features are independently tested on various classifiers: AdaBoost.M1, support vector machine and k-nearest neighbour. Features are also tested on a hybrid classifier formed by fusing all the mentioned earlier classifiers by the 'majority voting rule'. Fingerprint databases consisting of 185 real, 90 fun-doh and 150 gummy fingerprints are created. Experimental results indicate that, the performance of new liveness detection approach is very promising, as it needs only one fingerprint and no extra hardware to detect vitality.

Local binary pattern and wavelet-based spoof fingerprint detection

Ridgelet-based fake fingerprint detection

This paper proposes a texture-based method to spoof-proof a fingerprint biometric system. The fundamental basis of this anti-spoofing method is that, real fingerprint exhibits different textural characteristics from a spoof one. Textural measures based on wavelet energy signatures and gray level co-occurrence matrix (GLCM) features are used to characterize fingerprint texture. Dimensionalities of the feature sets are reduced by running Pudilpsilas sequential forward floating selection (SFFS) algorithm. We test two feature sets independently on various classifiers like: AdaBoost.M1, support vector machine and OneR. Then, we fuse all the mentioned classifiers using the ldquoproduct rulerdquo to form a hybrid classifier. Classification rates achieved for wavelet energy signatures range from ~94.35% to ~96.71%. Likewise, classification rates for GLCM features range from ~94.82% to ~97.65%. Thus, the performance of a proposed method is very promising and it can be efficiently used to spoof-proof a real-time fingerprint biometric system.

Shankar Bhausaheb Nikam

Papers

Texture and Wavelet-Based Spoof Fingerprint Detection for Fingerprint Biometric Systems

Fingerprint Liveness Detection Using Curvelet Energy and Co-Occurrence Signatures

Local binary pattern and wavelet-based spoof fingerprint detection

Ridgelet-based fake fingerprint detection

Wavelet energy signature and GLCM features-based fingerprint anti-spoofing