An evaluation of fake fingerprint databases utilizing SVM classification

With the increasing use of fingerprint authentication systems on portable devices, fake fingerprint detection has become growing important because fingerprints can be easily spoofed from a variety of available fabrication materials. Recently, many smartphones are hacked successfully by 2D fake fingerprint, which is a serious threat to authentication security. In order to enhance the robustness against fake fingerprint type, this paper proposes a novel 2D fake fingerprint detection method for portable devices based on improved light Convolutional Neural Networks (CNN). To evaluate the performance of the proposed method, a new 2D fake fingerprint dataset including three fabrication materials is created from capacitive fingerprint scanner. In addition, batch normalization and global average pooling are integrated to optimize the network. Experimental results show that the proposed method has high accuracy, strong robustness and good real-time performance, and can meet the requirements on portable devices.

Fingerprint is the most hopeful biometric authentication that can specifically identify a person from their exclusive features. In the proposed approach, a novel software-based classification method is presented to classify between fake and real fingerprint. The intention of the proposed system is to improve the security of biometric identification system. The statistical techniques are good for micro features but not well for macro. In this paper, we present a novel combination of local Haralick micro texture features with macro features derived from neighbourhood gray-tone difference matrix (NGTDM) to generate an effective feature vector. Combined extracted features of training and testing images are passed to support vector machine for discriminating live and fake fingerprints. The proposed approach is experimented and validated on ATVS dataset and LivDet2011 dataset. The proposed approach has achieved good accuracy and less error rate in comparison with previously studied techniques.

Automatic matching of poor quality latent fingerprints to rolled/slap fingerprints using an Automated Fingerprint Identification System (AFIS) is still far from satisfactory. Therefore, it is a common practice to have a latent examiner mark features on a latent for improving the hit rate of the AFIS. We propose a synergistic crowd powered latent identification framework where multiple latent examiners and the AFIS work in conjunction with each other to boost the identification accuracy of the AFIS. Given a latent, the candidate list output by the AFIS is used to determine the likelihood that a hit at rank-1 was found. A latent for which this likelihood is low is crowdsourced to a pool of latent examiners for feature markup. The manual markups are then input to the AFIS to increase the likelihood of making a hit in the reference database. Experimental results show that the fusion of an AFIS with examiner markups improves the rank-1 identification accuracy of the AFIS by 7.75% (using six markups) on the 500 ppi NIST SD27, 11.37% (using two markups) on the 1000 ppi ELFT-EFS public challenge database, and by 2.5% (using a single markup) on the 1000 ppi RS&A database against 250,000 rolled prints in the reference database.

Fingerprint recognition is very stable, reliable, and oldest biometric authentication technology that has been widely used in various civilian and government applications. Like other authentication technologies, fingerprint recognition is not totally free from spoofing. A fingerprint recognition system can be spoofed by placing artificially generated fingerprints of enrolled user at the sensor that are made up of materials like gelatine, silicon, wax or play-doh etc. Liveness detection is a technique to enhance the security of such systems by detecting physiological life signs in fingerprint samples to differentiate a real fingerprint from an artificial one. A live fingerprint image has the property to produce gray level variations along ridges due to presence of sweat pores, perspiration and skin quality (dry, wet) whereas a fake fingerprint has uniform gray levels that can give a fair idea about the liveness in input sample. In the proposed work, two fingerprints are captured by scanner at different time points (0 sec and 2 sec). In order to find their gray level variations, a gray level co-occurrence matrix of each fingerprint image is determined. After that, contrast value is computed from each matrix. If the difference in their contrast value is greater than threshold, then the input fingerprint sample is considered as live otherwise it is considered as fake. Liveness detection solution presented in this work helps to avoid direct attacks at fingerprint sensor that result in very secure and precise fingerprint detection. Keywords: Biometrics, Fingerprint detection, Liveness Detection, Spoofing

Fingerprint has been one of the most successful biometrics applied in both forensic law enforcement and security applications. Fingerprint acquisition, for many years, has been accomplished by first pressing a finger on a hard plane and then converting the image into a digital form. Recent developments in fingerprint acquisition technology have resulted in touchless live scan devices that generate 3D representation of fingerprints. To be compatible with legacy 2D fingerprint recognition techniques, the common way to develop an Automatic Fingerprint Identification System (AFIS) using 3D fingerprints is to unwrap 3D fingerprints into 2D equivalent images, then the existing algorithms for 2D fingerprint processing can be utilized. In this paper, we present some observations of 2D to 3D (unraveled 2D equivalent images from 3D model) fingerprints recognition on a small database collected using commercial scanners. False reject rate is adopted to measure the recognition performance and a commercial software VeriFinger is used for recognition. According to our experiments, the general performance of 2D to 3D fingerprints recognition is not comparable to that of 2D fingerprint recognition, specifically, the false reject rate is very high and the number of falsely extracted minutiae is large. Besides, we have analyzed possible reasons for the poor performance and pointed out some future directions regarding 3D fingerprint technology.

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.

Human fingerprints are considered to be the most extensively used modality in state-of-the-art biometric based security systems. A newly developed potential threat occurs in the form of specialized fake fingerprint, known as the double-identity fingerprint which is created by careful alignment of two genuine fingerprints to ensure smooth ridges at the intersection points and with high chance of being matched by the criminal as well as accomplice. In this paper, we propose a countermeasure to resolve this problem with generalized Benford's law in conjunction with support vector machine classification. The generalized Benford's law is found to validate the probability distribution of first nonzero digits from block-DCT coefficients of genuine fingerprint images. The experimental results support that the synthesized fake double-identity fingerprints do not inherently follow the generalized Benford's law and hence the proposed method can be effective to discriminate from the genuine class with high accuracy.

The advancement of science and technology has made the reliable individual recognition and identification systems to become very popular. From the various biometric characteristics, fingerprint is one of the popular method because of its easiness and not much effort is required to acquire fingerprint. First step for an Automated Fingerprint Identification System (AFIS) is the segmentation of fingerprint from the acquired image. During fingerprint segmentation process the input image is decomposed into foreground and background areas. The foreground area contains information that are needed in the automatic fingerprint recognition systems. However, the background is a noisy region that contributes to the extraction of false features. So in an AFIS, fingerprint image segmentation plays an important role in carefully separating ridge like part (foreground) from noisy background. Gradient based method is commonly used for segmentation process. Since gradient estimation is erroneous in noisy images, the study proposes a combination of gradient mask and morphological operations to segment fingerprint foreground effectively. The results obtained prove that the new method is suited for fingerprint segmentation.

Fingerprint recognition has wide application prospect in all fields which contain identity authentication. Construction of accurate and reliable，safe and Practical automatic fingerprint identification system(AFIS) has become researc hotspot. Although theoretical research and application developmen of AFIS have got a significant Progress，accuracy of the algorithm and proeessing speeds till need to be improved. In this paper, fingerprint image preprocessing algorithms，fingerprint singular Points and minutiae extraction algorithm and fingerprint matching algorithm are analyzed and discussed in detail.

In recent decades, the classification of fake fingerprints and real fingerprint images has become an attractive research topic because of the most advanced threats. A number of research works have been carried out to classify fake and real fingerprints. But, most of the existing techniques did not utilize swarm intelligence techniques in their fingerprint classification system. Swarm intelligence has been widely used in various applications due to its robustness and potential in solving a complex optimization problem. The proposed classification methodology comprises of four steps, namely image preprocessing, feature extraction, feature selection and classification. This work uses efficient min-max normalization and median filtering for preprocessing, and multiple static features are extracted from Gabor filtering. To perform multiple static feature selection, Artificial Bee Colony (ABC) and Modified Artificial Bee Colony (MABC) optimization algorithm is used in this work to choose the best optimal static features on the basis of specific fitness values. This approach uses Fuzzy Feed Forward Neural Network (FFFNN) for classification and classification of feature vector into fake and real fingerprint images by the semi supervised graph based classification approach which enables efficient classification of real and fake fingerprints and its performance is compared with the SVM classifier. Evaluation of the performance has been carried out by taking the fingerprint images that had been collected from the FVC2000 and synthetically generated database by employing the SFinGE. The results showed that the proposed work provided improvised and enhanced results with respective to characteristics like sensitivity, precision, specification as well as classification accuracy.

Fingerprint is widely used physical human trait for uniquely identification and verification of human but use of fingerprint is becoming very challenging due to spoofing attacks. In this paper, we present new combination of local diagonal extrema pattern and local phase quantization descriptors to extract the features to generate feature vector for training and testing fingerprint images. Local diagonal extrema pattern finds the relationship between center pixels with diagonal neighboring pixels and local phase quantization extracts the local phase information by using short-term Fourier transform. LDEP reduces the dimensionality problem and forms a good combination of feature descriptor with LPQ. Combined extracted features of training and testing images using both descriptors are passed to Support Vector Machine for discriminating live and fake fingerprints. Experiments have been performed on LivDet2009 dataset and results show the effective and efficient performance of the proposed system. The proposed system achieved good accuracy and very less error rate in comparison to the different descriptors.

This paper mainly studies how to detect a wide variety of ships from the ship-borne infrared images in order to implement sea monitoring. Different types of ships have significant differences in their appearance. The traditional detection method which uses the global texture features of the object is not suitable to detect varied ships. This paper presents a novel detection algorithm which extracts spatial partial texture features trained by Adaboost to establish the ship model for detection. We first extract all the partial regions of the object through random traversal, and then extract the texture features by using the "Uniform LBP" operator. Compared to the traditional way, we save each partial feature individually as one feature vector, which not only reduces the vector dimension but also highlights the key regions when the partial regions with strong generality are selected by Adaboost at the second step. Finally, the selected partial features are boosted with weights to establish ship model for the ship detection. The proposed approach is efficient and robust in the infrared ship detection.

Content-Based Image Retrieval (CBIR) has become one of the trending areas of research in computer vision. In traditional CBIR, the features in a spatial domain, such as color, texture, shape, and point features, are extracted. It is often considered that apart from the spatial features, the features extracted from the frequency domain of the images can give further information on the features of an image. In this paper, we are proposing two novel methods for the purpose of feature extraction from the 2-dimensional Discrete Cosine Transform (DCT) of an image: DCT_256_Zigzag and DCT_256_2 × 2. These methods take into consideration the lower frequencies in order to determine the features in the frequency domain. The advantage of using the zigzag scanning is to have the maximum low frequency values having Higher Energies comparatively. These two features are combined with two of the existing spatial domain features: Local Binary Patterns (LBP) and Interchannel voting features to generate a global feature vector for an image. For a query image, its feature vector is compared with feature vectors of every other image in the database using d1-distance, and the images with least distance are considered most similar images to the query image. To evaluate the efficiency of these two methods, five standard performance measures such as Average Precision Rate (APR), Average Recall Rate (ARR), F-Measure, Average Normalized Modified Retrieval Rank (ANMRR), and Total Minimum Retrieval Epoch (TMRE) are used. Six benchmark image datasets Corel-1000, Corel-5000, Corel-10000, VisTex, STex, and Color-Brodatz are used to corroborate the performance of these methods.KeywordsCBIRDCTFrequency domainInterchannel votingLBPTMRE

Individual recognition systems with high-speed and high-accuracy have been recently demanded in the automatic logging into a PC, the immigration at the airport, the access control and diligence & indolence management in an office, and so on. Biometric authentication is now being regarded as the most valid method because of the receptivity, individuality and invariability of biometric identifiers. Various types of the individual recognition systems based on biometrics have been studied and partially realized. Fingerprints, faces, hand geometry, irises, vein patterns, gait, signatures, etc., are known as biometric identifiers. In particular, the fingerprint recognition system has been widely used because of its high reliability and reasonable price (Maltoni et al., 2003a; Jain et al., 2010). The fingerprint recognition methods can be classified into the following three types: (i) the minutiae-based, (ii) the frequency-based and (iii) the image-based methods. The minutiae-based method is mainly being used in the practical fingerprint recognition system. For example, a memetic fingerprint matching algorithm has been recently proposed to identify the optimal or near optimal global matching between two minutiae sets (Sheng et al., 2007). A matching technique for fingerprint recognition using the Minutia CylinderCode (MCC), which is based on 3D data structures built from minutiae distances and angles, has also been made a proposal (Cappelli et al., 2010) to exclude the drawbacks in the fingerprint authentication using local minutiae structures. Moreover, a fingerprint verification using spectral minutiae representations has been suggested to overcome translation, rotation and scaling which are the drawbacks of minutiae-based algorithms (Xu et al., 2009a, 2009b). The frequency-based method, such as the frequency analysis method, is also being used in the practical fingerprint recognition system in order to secretly hide the original fingerprint information (Takeuchi et al., 2007). Recently, a fingerprint recognition method based on melfrequency cepstral coefficients and polynomial shape coefficients has been proposed because of the robustness to noise and the insensivity to translation (Hashad et al., 2010). The image-based method has been being studied to improve the accuracy in the fingerprint recognition. For example, an enhanced image-based algorithm for fingerprint verification based on invariant moment features has been recently proposed to improve matching accuracy and processing speed (Yang & Park, 2008a, 2008b). A novel image-based fingerprint matcher based on the minutiae alignment has also been made a proposal to improve the verification performance (Nanni & Lumini, 2009).

Contactless fingerprint recognition is highly promising and an essential component in the automatic fingerprint identification system. However, due to the inherent characteristic of perspective distortions of contactless fingerprints, achieving a highly accurate contactless fingerprint recognition system is very challenging. In this paper, we propose a robust contactless fingerprint recognition method based on global minutia topology and loose genetic algorithm. In order to avoid the inaccurate minutiae alignment problem suffered in conventional transformation-based methods, the minutiae correspondence is established by optimizing an energy function of the similarity matrix. We define an innovative similarity matrix based on both minutiae and minutia-pairs, which takes the global minutia topology into account. By adopting a distortion-free feature of ridge count to define the similarity, the problem of perspective distortions is effectively overcome. To solve the optimization, we propose a new genetic algorithm (GA) named loose GA with new mutation and crossover operators. We also propose a strict minutia-pair expanding algorithm to enhance the reliability of the minutiae correspondence. For recognition, a metric for measuring comparison scores which takes advantage of both the global topological similarity and the number of corresponding minutiae is proposed. We evaluate our method using two contactless fingerprint benchmark databases and achieve competitive performances in comparison with the state-of-the-art methods.