Showing papers on "Fingerprint recognition published in 2011"

Latent Fingerprint Matching

Abstract: Latent fingerprint identification is of critical importance to law enforcement agencies in identifying suspects: Latent fingerprints are inadvertent impressions left by fingers on surfaces of objects. While tremendous progress has been made in plain and rolled fingerprint matching, latent fingerprint matching continues to be a difficult problem. Poor quality of ridge impressions, small finger area, and large nonlinear distortion are the main difficulties in latent fingerprint matching compared to plain or rolled fingerprint matching. We propose a system for matching latent fingerprints found at crime scenes to rolled fingerprints enrolled in law enforcement databases. In addition to minutiae, we also use extended features, including singularity, ridge quality map, ridge flow map, ridge wavelength map, and skeleton. We tested our system by matching 258 latents in the NIST SD27 database against a background database of 29,257 rolled fingerprints obtained by combining the NIST SD4, SD14, and SD27 databases. The minutiae-based baseline rank-1 identification rate of 34.9 percent was improved to 74 percent when extended features were used. In order to evaluate the relative importance of each extended feature, these features were incrementally used in the order of their cost in marking by latent experts. The experimental results indicate that singularity, ridge quality map, and ridge flow map are the most effective features in improving the matching accuracy.

Fingerprint Reconstruction: From Minutiae to Phase

Abstract: Fingerprint matching systems generally use four types of representation schemes: grayscale image, phase image, skeleton image, and minutiae, among which minutiae-based representation is the most widely adopted one. The compactness of minutiae representation has created an impression that the minutiae template does not contain sufficient information to allow the reconstruction of the original grayscale fingerprint image. This belief has now been shown to be false; several algorithms have been proposed that can reconstruct fingerprint images from minutiae templates. These techniques try to either reconstruct the skeleton image, which is then converted into the grayscale image, or reconstruct the grayscale image directly from the minutiae template. However, they have a common drawback: Many spurious minutiae not included in the original minutiae template are generated in the reconstructed image. Moreover, some of these reconstruction techniques can only generate a partial fingerprint. In this paper, a novel fingerprint reconstruction algorithm is proposed to reconstruct the phase image, which is then converted into the grayscale image. The proposed reconstruction algorithm not only gives the whole fingerprint, but the reconstructed fingerprint contains very few spurious minutiae. Specifically, a fingerprint image is represented as a phase image which consists of the continuous phase and the spiral phase (which corresponds to minutiae). An algorithm is proposed to reconstruct the continuous phase from minutiae. The proposed reconstruction algorithm has been evaluated with respect to the success rates of type-I attack (match the reconstructed fingerprint against the original fingerprint) and type-II attack (match the reconstructed fingerprint against different impressions of the original fingerprint) using a commercial fingerprint recognition system. Given the reconstructed image from our algorithm, we show that both types of attacks can be successfully launched against a fingerprint recognition system.

Meta-Recognition: The Theory and Practice of Recognition Score Analysis

Abstract: In this paper, we define meta-recognition, a performance prediction method for recognition algorithms, and examine the theoretical basis for its postrecognition score analysis form through the use of the statistical extreme value theory (EVT). The ability to predict the performance of a recognition system based on its outputs for each match instance is desirable for a number of important reasons, including automatic threshold selection for determining matches and nonmatches, and automatic algorithm selection or weighting for multi-algorithm fusion. The emerging body of literature on postrecognition score analysis has been largely constrained to biometrics, where the analysis has been shown to successfully complement or replace image quality metrics as a predictor. We develop a new statistical predictor based upon the Weibull distribution, which produces accurate results on a per instance recognition basis across different recognition problems. Experimental results are provided for two different face recognition algorithms, a fingerprint recognition algorithm, a SIFT-based object recognition system, and a content-based image retrieval system.

Fingerprint Indexing Based on Minutia Cylinder-Code

Abstract: This paper proposes a new hash-based indexing method to speed up fingerprint identification in large databases. A Locality-Sensitive Hashing (LSH) scheme has been designed relying on Minutiae Cylinder-Code (MCC), which proved to be very effective in mapping a minutiae-based representation (position/angle only) into a set of fixed-length transformation-invariant binary vectors. A novel search algorithm has been designed thanks to the derivation of a numerical approximation for the similarity between MCC vectors. Extensive experimentations have been carried out to compare the proposed approach against 15 existing methods over all the benchmarks typically used for fingerprint indexing. In spite of the smaller set of features used (top performing methods usually combine more features), the new approach outperforms existing ones in almost all of the cases.

Fingerprint sensor and integratable electronic display

Abstract: A fingerprint sensor which includes a conductive layer which is incorporatable within an electronic display is disclosed. The fingerprint sensor also includes a controller coupled to the conductive layer to capture a fingerprint image and can further be adapted to control the display.

Minutiae Extraction from Fingerprint Images - a Review

Abstract: Fingerprints are the oldest and most widely used form of biometric identification. Everyone is known to have unique, immutable fingerprints. As most Automatic Fingerprint Recognition Systems are based on local ridge features known as minutiae, marking minutiae accurately and rejecting false ones is very important. However, fingerprint images get degraded and corrupted due to variations in skin and impression conditions. Thus, image enhancement techniques are employed prior to minutiae extraction. A critical step in automatic fingerprint matching is to reliably extract minutiae from the input fingerprint images. This paper presents a review of a large number of techniques present in the literature for extracting fingerprint minutiae. The techniques are broadly classified as those working on binarized images and those that work on gray scale images directly.

Defending Against Fingerprint-Copy Attack in Sensor-Based Camera Identification

Abstract: Sensor photoresponse nonuniformity has been proposed as a unique identifier (fingerprint) for various forensic tasks, including digital-camera ballistics in which an image is matched to the specific camera that took it. The problem investigated here concerns the situation when an adversary estimates the sensor fingerprint from a set of images and superimposes it onto an image from a different camera to frame an innocent victim. This paper proposes a reliable method for detecting such fake fingerprints under rather mild and general assumptions about the adversary's activity and the means available to the victim. The proposed method is subjected to experiments to evaluate its reliability as well as its limitations. The conclusion that can be made from this study is that planting a sensor fingerprint in an image without leaving a trace is significantly more difficult than previously thought.

Selecting a Reference High Resolution for Fingerprint Recognition Using Minutiae and Pores

Abstract: High-resolution automated fingerprint recognition systems (AFRSs) offer higher security because they are able to make use of level-3 features, such as pores, that are not available in lower resolution ( <; 500-dpi) images. One of the main parameters affecting the quality of a digital fingerprint image and issues such as cost, interoperability, and performance of an AFRS is the choice of image resolution. In this paper, we identify the optimal resolution for an AFRS using the two most representative fingerprint features: minutiae and pores. We first designed a multiresolution fingerprint acquisition device to collect fingerprint images at multiple resolutions and captured fingerprints at various resolutions but at a fixed image size. We then carried out a theoretical analysis to identify the minimum required resolution for fingerprint recognition using minutiae and pores. After experiments on our collected fingerprint images and applying three requirements for the proportions of minutiae and pores that must be retained in a fingerprint image, we recommend a reference resolution of 800 dpi. Subsequent tests have further confirmed the proposed reference resolution.

KL-divergence kernel regression for non-Gaussian fingerprint based localization

Abstract: Various methods have been developed for indoor localization using WLAN signals. Algorithms that fingerprint the Received Signal Strength Indication (RSSI) of WiFi for different locations can achieve tracking accuracies of the order of a few meters. RSSI fingerprinting suffers though from two main limitations: first, as the signal environment changes, so does the fingerprint database, which requires regular updates; second, it has been reported that, in practice, certain devices record more complex (e.g bimodal) distributions of WiFi signals, precluding algorithms based on the mean RSSI. In this article, we propose a simple methodology that takes into account the full distribution for computing similarities among fingerprints using Kullback-Leibler divergence, and that performs localization through kernel regression. Our method provides a natural way of smoothing over time and trajectories. Moreover, we propose unsupervised KL-divergence-based recalibration of the training fingerprints. Finally, we apply our method to work with histograms of WiFi connections to access points, ignoring RSSI distributions, and thus removing the need for recalibration. We demonstrate that our results outperform nearest neighbors or Kalman and Particle Filters, achieving up to 1m accuracy in office environments. We also show that our method generalizes to non-Gaussian RSSI distributions.

Fingerprint Matching Incorporating Ridge Features With Minutiae

Abstract: This paper introduces a novel fingerprint matching algorithm using both ridge features and the conventional minutiae feature to increase the recognition performance against nonlinear deformation in fingerprints. The proposed ridge features are composed of four elements: ridge count, ridge length, ridge curvature direction, and ridge type. These ridge features have some advantages in that they can represent the topology information in entire ridge patterns existing between two minutiae and are not changed by nonlinear deformation of the finger. For extracting ridge features, we also define the ridge-based coordinate system in a skeletonized image. With the proposed ridge features and conventional minutiae features (minutiae type, orientation, and position), we propose a novel matching scheme using a breadth-first search to detect the matched minutiae pairs incrementally. Following that, the maximum score is computed and used as the final matching score of two fingerprints. Experiments were conducted for the FVC2002 and FVC2004 databases to compare the proposed method with the conventional minutiae-based method. The proposed method achieved higher matching scores. Thus, we conclude that the proposed ridge feature gives additional information for fingerprint matching with little increment in template size and can be used in conjunction with existing minutiae features to increase the accuracy and robustness of fingerprint recognition systems.

Fingerprint Recognition with Embedded Cameras on Mobile Phones

Abstract: Mobile phones with a camera function are capable of capturing image and processing tasks. Fingerprint recognition has been used in many different applications where high security is required. A first step towards a novel biometric authentication approach applying cell phone cameras capturing fingerprint images as biometric traits is proposed. The proposed method is evaluated using 1320 fingerprint images from each embedded capturing device. Fingerprints are collected by a Nokia N95 and a HTC Desire. The overall results of this approach show a biometric performance with an Equal Error Rate (EER) of 4.5% by applying a commercial extractor/comparator and without any preproccesing on the images.

Method of Executing Software Functions Using Biometric Detection and Related Electronic Device

Abstract: A method of executing software functions on an electronic device having biometric detection includes receiving touch input from one or more fingers of a user on a fingerprint sensor of the electronic device and recognizing one or more fingerprints and recognizing a gesture in the received touch input. The method further includes performing a fingerprint comparison to compare the one or more recognized fingerprints to contents of a database, performing a gesture comparison to compare the recognized gesture to contents of the database, determining a matching software function according to results of the fingerprint comparison and the gesture comparison, and executing the matching software function.

Latent fingerprint matching using descriptor-based hough transform

Abstract: Identifying suspects based on impressions of fingers lifted from crime scenes (latent prints) is extremely important to law enforcement agencies. Latents are usually partial fingerprints with small area, contain nonlinear distortion, and are usually smudgy and blurred. Due to some of these characteristics, they have a significantly smaller number of minutiae points (one of the most important features in fingerprint matching) and therefore it can be extremely difficult to automatically match latents to plain or rolled fingerprints that are stored in law enforcement databases. Our goal is to develop a latent matching algorithm that uses only minutiae information. The proposed approach consists of following three modules: (i) align two sets of minutiae by using a descriptor-based Hough Transform; (ii) establish the correspondences between minutiae; and (iii) compute a similarity score. Experimental results on NIST SD27 show that the proposed algorithm outperforms a commercial fingerprint matcher.

Improving Fingerprint Orientation Extraction

Abstract: Computation of local orientations is a primary step in fingerprint recognition. A large number of approaches have been proposed in the literature, but no systematic quantitative evaluations have been done yet. We implemented and tested several well know methods and a plethora of their variants over a novel, specifically designed, benchmark, made available in the FVC-onGoing framework. We proved that parameter optimizations, pre- and post-processing stages can markedly improve accuracy of the baseline methods on bad quality fingerprints. Finally, in this paper we propose a novel adaptive method which selectively exploits accuracy of local-based analysis and learning-based global methods, thus achieving the overall best performance on a challenging dataset.

Erratum to “Defending Against Fingerprint-Copy Attack in Sensor-Based Camera Identification” [Mar 11 227-236]

Abstract: Due to a production error, the above titled paper (ibid., vol. 6, no. 1, pp. 227-236, Mar. 11), was published as a correspondence in the March 2011 issue of IEEE Transactions on Information Forensics and Security. This paper was actually accepted as a Regular Paper and should have been published as such.

Electronic Device with Multimode Fingerprint Reader

Abstract: An electronic device may operate a fingerprint reader in a stationary finger mode in which the fingerprint reader captures a fingerprint from a user's finger while the user's finger is in a stationary position and may operate the fingerprint reader in a moving finger mode in which the fingerprint reader captures a fingerprint from the user's finger while the user is swiping the finger across the fingerprint reader. The electronic device may use the moving finger mode when performing sensitive operations such as operations related to financial transactions. The electronic device may include near field communications circuitry. When activity is detected using the near field communications circuitry, the fingerprint reader may be operated in the moving finger mode. The fingerprint sensor may be activated when a proximity sensor detects the presence of a finger. Different actions may be taken by the device in response to detection of different fingerprints.

Fingerprint Recognition using Image Segmentation

Abstract: This paper is a study and implementation of a fingerprint recognition system based on Minutiae based matching quite frequently used in various fingerprint algorithms and techniques. The approach mainly involves extraction of minutiae points from the sample fingerprint images and then performing fingerprint matching based on the number of minutiae pairings among two fingerprints in question.

A Fingerprint Recognition Scheme Based on Assembling Invariant Moments for Cloud Computing Communications

Abstract: In cloud computing communications, information security entails the protection of information elements (e.g., multimedia data), only authorized users are allowed to access the available contents. Fingerprint recognition is one of the popular and effective approaches for priori authorizing the users and protecting the information elements during the communications. However, traditional fingerprint recognition approaches have demerits of easy losing rich information and poor performances due to the complex inputs, such as image rotation, incomplete input image, poor quality image enrollment, and so on. In order to overcome these shortcomings, in this paper, a new fingerprint recognition scheme based on a set of assembled invariant moment (geometric moment and Zernike moment) features to ensure the secure communications is proposed. And the proposed scheme is also based on an effective preprocessing, the extraction of local and global features and a powerful classification tool, thus it is able to handle the various input conditions encountered in the cloud computing communication. The experimental results show that the proposed method has a higher matching accuracy comparing with traditional or individual feature based methods on public databases.

Separating Overlapped Fingerprints

Abstract: Fingerprint images generally contain either a single fingerprint (e.g., rolled images) or a set of nonoverlapped fingerprints (e.g., slap fingerprints). However, there are situations where several fingerprints overlap on top of each other. Such situations are frequently encountered when latent (partial) fingerprints are lifted from crime scenes or residue fingerprints are left on fingerprint sensors. Overlapped fingerprints constitute a serious challenge to existing fingerprint recognition algorithms, since these algorithms are designed under the assumption that fingerprints have been properly segmented. In this paper, a novel algorithm is proposed to separate overlapped fingerprints into component or individual fingerprints. The basic idea is to first estimate the orientation field of the given image with overlapped fingerprints and then separate it into component orientation fields using a relaxation labeling technique. We also propose an algorithm to utilize fingerprint singularity information to further improve the separation performance. Experimental results indicate that the algorithm leads to good separation of overlapped fingerprints that leads to a significant improvement in the matching accuracy.

Fast and Accurate Fingerprint Indexing Based on Ridge Orientation and Frequency

Abstract: This paper describes a new fingerprint indexing approach based on vector and scalar features, obtained from ridge-line orientations and frequencies. A carefully designed set of features and ad-hoc score measures allow the proposed indexing algorithm to be extremely effective and efficient, as confirmed by the results of extensive experiments. The new method markedly outperforms competing state-of-the-art techniques over six publicly available data sets. Furthermore, it can scale to large databases without losing accuracy: on a standard PC, a search over one million fingerprints takes less than 1 s.

Contactless fingerprint identification using level zero features

Abstract: Several recent research efforts in the biometrics have focused on developing personal identification using very low-resolution imaging resulting from widely deployed surveillance cameras and mobile devices. Identification of human faces using such low-resolution imaging has shown promising results and has shown its utility for range of applications (surveillance). This paper investigates contactless identification of such low resolution (∼ 50 dpi) fingerprint images acquired using webcam. The acquired images are firstly subjected to robust preprocessing steps to extract region of interest and normalize uneven illumination. We extract localized feature information and effectively incorporate this local information into matching stage. The experimental results are presented on two session database of 156 subjects acquired over a period of 11 months and achieve average rank-one identification accuracy of 93.97%. The achieved results are highly promising to invite attention for range of applications, including surveillance, and sprung new directions for further research efforts.

Indexing fingerprints using minutiae quadruplets

Abstract: The computational complexity of matching an input fingerprint against every entry in a large-scale fingerprint database can be prohibitive. In fingerprint indexing, a small set of candidate fingerprints is selected from the database and only images in this set are compared against the input probe fingerprint thereby avoiding an exhaustive matching process. In this paper, a new structure named “minutiae quadruplet” is proposed for indexing fingerprints and is used in combination with a clustering technique to filter a fingerprint database. The proposed indexing algorithm is evaluated on all datasets in the Fingerprint Verification Competition (FVC) 2000, 2002 and 2004 databases. The high hit rates achieved at low penetration rates suggest that the proposed algorithm is beneficial for indexing. Indeed, it was observed that for 50% of the fingerprints, in most of the datasets, the penetration rate was less than 5.5% at a 100% hit rate. The robust performance across different databases suggests that the indexing algorithm can be adapted for use in large-scale databases.

A Novel design of Electronic Voting System Using Fingerprint

Abstract: The heart of democracy is voting. The heart of voting is trust that each vote is recorded and tallied with accuracy and impartiality. The accuracy and impartiality are tallied in high rate with biometric system. Among these biometric signs, fingerprint has been researched the longest period of time, and shows the most promising future in real-world applications. Because of their uniqueness and consistency over time, fingerprints have been used for identification over time. However, because of the complex distortions among the different impression of the same finger in real life, fingerprint recognition is still a challenging problem. Hence in this study, the authors are interested in designing and analysing the Electronic Voting System based on the fingerprint minutiae which is the core in current modern approach for fingerprint analysis. The new design is analysed by conducting pilot election among a class of students for selecting their representative. Various analysis predicted shows that the proposed electronic voting system resolves many issues of the current system with the help of biometric technology.

Multimodal biometric method that combines veins, prints, and shape of a finger

Abstract: Multimodal biometrics provides high recognition accuracy and population coverage by using various biometric features. A single finger contains finger veins, fingerprints, and finger geometry features; by using multimodal biometrics, information on these multiple features can be simultaneously obtained in a short time and their fusion can outperform the use of a single feature. This paper proposes a new finger recognition method based on the score-level fusion of finger veins, fingerprints, and finger geometry features. This research is novel in the following four ways. First, the performances of the finger-vein and fingerprint recognition are improved by using a method based on a local derivative pattern. Second, the accuracy of the finger geometry recognition is greatly increased by combining a Fourier descriptor with principal component analysis. Third, a fuzzy score normalization method is introduced; its performance is better than the conventional Z-score normalization method. Fourth, finger-vein, fingerprint, and finger geometry recognitions are combined by using three support vector machines and a weighted SUM rule. Experimental results showed that the equal error rate of the proposed method was 0.254%, which was lower than those of the other methods.

Score Level Fusion of Fingerprint and Finger Vein Recognition

Abstract: Unimodal biometric recognition is not able to meet the performance requirements in most cases with its application becomes more and more broadly. Recognition based on multimodal biometrics represents an emerging trend recently. In the paper, we propose multimodal biometrics recognition based on score level fusion of fingerprint and finger vein, since fingerprint recognition and finger vein recognition are complementary in several aspects. Experimental results based on homologous biometrics database demonstrate that the fusion of fingerprint and finger vein leads to a dramatically improvement in performance.

Wi-Fi fingerprint-based approaches following log-distance path loss model for indoor positioning

Abstract: Wi-Fi fingerprint based indoor positioning methods exploits naive signal strength in each location to predict user's location. However, using naive signal strength may limit the prediction accuracy, because the Wi-Fi signals follow the Log-distance path loss model in signal propagation. In this research, we propose to generate Wi-Fi fingerprint with the consideration of radio propagation model to reflex the characteristics of Wi-Fi signal. The evaluation result shows that following log-distance path loss model improves the prediction accuracy in fingerprint based indoor positioning.

Authorship identification and author fuzzy “fingerprints”

Abstract: Fingerprint identification is a well-known technique in forensic sciences. The basic idea of identifying a subject based on a set of features left by the subject actions or behavior can be applied to other domains. Identifying text authorship based on an author “fingerprint” is one such application. This paper considers the problem of extracting “fingerprints” from texts and matching them with those obtained from a set of known authors. It presents an innovative fuzzy fingerprint algorithm based on vector valued fuzzy sets. Words and other stylometric features are used to create the fingerprint. The implementation is based on an approximated fast and compact algorithm that allows the method to be used on near real time, even for a large number of authors and texts.

Performance comparison of denoising filters for source camera identification

Abstract: Source identification for digital content is one of the main branches of digital image forensics. It relies on the extraction of the photo-response non-uniformity (PRNU) noise as a unique intrinsic fingerprint that efficiently characterizes the digital device which generated the content. Such noise is estimated as the difference between the content and its de-noised version obtained via denoising filter processing. This paper proposes a performance comparison of different denoising filters for source identification purposes. In particular, results achieved with a sophisticated 3D filter are presented and discussed with respect to state-of-the-art denoising filters previously employed in such a context.