Minutiae Extraction from Fingerprint Images - a Review

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.Comment: 12 pages; IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 5, September 201

Two-stage quality adaptive fingerprint image enhancement using Fuzzy c-means clustering based fingerprint quality analysis

Fingerprint recognition techniques are immensely dependent on quality of the fingerprint images. To improve the performance of recognition algorithm for poor quality images an efficient enhancement algorithm should be designed. Performance improvement of recognition algorithm will be more if enhancement process is adaptive to the fingerprint quality (wet, dry or normal). In this paper, a quality adaptive fingerprint enhancement algorithm is proposed. The proposed fingerprint quality assessment algorithm clusters the fingerprint images in appropriate quality class of dry, wet, normal dry, normal wet and good quality using fuzzy c-means technique. It considers seven features namely, mean, moisture, variance, uniformity, contrast, ridge valley area uniformity and ridge valley uniformity into account for clustering the fingerprint images in appropriate quality class. Fingerprint images of each quality class undergo through a two-stage fingerprint quality enhancement process. A quality adaptive preprocessing method is used as front-end before enhancing the fingerprint images with Gabor, short term Fourier transform and oriented diffusion filtering based enhancement techniques. Experimental results show improvement in the verification results for FVC2004 datasets. Significant improvement in equal error rate is observed while using quality adaptive preprocessing based approaches in comparison to the current state-of-the-art enhancement techniques.Comment: 34 pages, 8 figures, Submitted to Image and Vision Computin

An Effective Fingerprint Verification Technique

This paper presents an effective method for fingerprint verification based on a data mining technique called minutiae clustering and a graph-theoretic approach to analyze the process of fingerprint comparison to give a feature space representation of minutiae and to produce a lower bound on the number of detectably distinct fingerprints. The method also proving the invariance of each individual fingerprint by using both the topological behavior of the minutiae graph and also using a distance measure called Hausdorff distance.The method provides a graph based index generation mechanism of fingerprint biometric data. The self-organizing map neural network is also used for classifying the fingerprints.Comment: Submitted to Journal of Computer Science and Engineering, see http://sites.google.com/site/jcseuk/volume-1-issue-1-may-201

Secure Iris Authentication Using Visual Cryptography

Biometrics deal with automated methods of identifying a person or verifying the identity of a person based on physiological or behavioral characteristics. Visual cryptography is a secret sharing scheme where a secret image is encrypted into the shares which independently disclose no information about the original secret image. As biometric template are stored in the centralized database, due to security threats biometric template may be modified by attacker. If biometric template is altered authorized user will not be allowed to access the resource. To deal this issue visual cryptography schemes can be applied to secure the iris template. Visual cryptography provides great means for helping such security needs as well as extra layer of authentication.Comment: IEEE Publication format, ISSN 1947 5500, http://sites.google.com/site/ijcsis

Discrete Logarithmic Fuzzy Vault Scheme

In this paper a three fuzzy vault schemes which integrated with discrete logarithmic encryption scheme are proposed. In the first scheme, the message m is encoded with discrete logarithmic encryption scheme using randomly generated identity key \k{appa} for every message and then divided into non-overlapping segments. In the second scheme, the message is divided into non-overlapping segments and each segment is encoded with discrete logarithmic encryption scheme using the randomly generated identity key \k{appa}. In the third scheme, the message is divided into non-overlapping segments where even segments are encoded with identity key \k{appa}_even and odd segments are encoded with identity key \k{appa}_odd. Identity keys \k{appa}_even and \k{appa}_odd are randomly generated for every message. Finally, the encoded segments are declared as coefficients of a polynomial of specific degree. In all proposed schemes, elements of locking set A are used as X-coordinate values to compute evaluations of the polynomial by projecting elements of A onto points lying on the polynomial. A large number of random chaff points that do not lie on the polynomial are added to create noise to hide the encoded segments. Security analysis has shown the proposed scheme enjoys provable security over classical fuzzy vaults

Skilled Impostor Attacks Against Fingerprint Verification Systems And Its Remedy

Fingerprint verification systems are becoming ubiquitous in everyday life. This trend is propelled especially by the proliferation of mobile devices with fingerprint sensors such as smartphones and tablet computers, and fingerprint verification is increasingly applied for authenticating financial transactions. In this study we describe a novel attack vector against fingerprint verification systems which we coin skilled impostor attack. We show that existing protocols for performance evaluation of fingerprint verification systems are flawed and as a consequence of this, the system's real vulnerability is systematically underestimated. We examine a scenario in which a fingerprint verification system is tuned to operate at false acceptance rate of 0.1% using the traditional verification protocols with random impostors (zero-effort attacks). We demonstrate that an active and intelligent attacker can achieve a chance of success in the area of 89% or more against this system by performing skilled impostor attacks. We describe a new protocol for evaluating fingerprint verification performance in order to improve the assessment of potential and limitations of fingerprint recognition systems. This new evaluation protocol enables a more informed decision concerning the operating threshold in practical applications and the respective trade-off between security (low false acceptance rates) and usability (low false rejection rates). The skilled impostor attack is a general attack concept which is independent of specific databases or comparison algorithms. The proposed protocol relying on skilled impostor attacks can directly be applied for evaluating the verification performance of other biometric modalities such as e.g. iris, face, ear, finger vein, gait or speaker recognition

FingerNet: An Unified Deep Network for Fingerprint Minutiae Extraction

Minutiae extraction is of critical importance in automated fingerprint recognition. Previous works on rolled/slap fingerprints failed on latent fingerprints due to noisy ridge patterns and complex background noises. In this paper, we propose a new way to design deep convolutional network combining domain knowledge and the representation ability of deep learning. In terms of orientation estimation, segmentation, enhancement and minutiae extraction, several typical traditional methods performed well on rolled/slap fingerprints are transformed into convolutional manners and integrated as an unified plain network. We demonstrate that this pipeline is equivalent to a shallow network with fixed weights. The network is then expanded to enhance its representation ability and the weights are released to learn complex background variance from data, while preserving end-to-end differentiability. Experimental results on NIST SD27 latent database and FVC 2004 slap database demonstrate that the proposed algorithm outperforms the state-of-the-art minutiae extraction algorithms. Code is made publicly available at: https://github.com/felixTY/FingerNet

Improved Dynamic Time Warping (DTW) Approach for Online Signature Verification

Online signature verification is the process of verifying time series signature data which is generally obtained from the tablet-based device. Unlike offline signature images, the online signature image data consists of points that are arranged in a sequence of time. The aim of this research is to develop an improved approach to map the strokes in both test and reference signatures. Current methods make use of the Dynamic Time Warping (DTW) algorithm and its variant to segment them before comparing each of its data dimension. This paper presents a modified DTW algorithm with the proposed Lost Box Recovery Algorithm aims to improve the mapping performance for online signature verificationComment: This paper is first author thesis pape

An Effective Fingerprint Classification and Search Method

This paper presents an effective fingerprint classification method designed based on a hierarchical agglomerative clustering technique. The performance of the technique was evaluated in terms of several real-life datasets and a significant improvement in reducing the misclassification error has been noticed. This paper also presents a query based faster fingerprint search method over the clustered fingerprint databases. The retrieval accuracy of the search method has been found effective in light of several real-life databases.Comment: 10 pages, 8 figures, 6 tables, referred journal publicatio

Performance of the Fuzzy Vault for Multiple Fingerprints (Extended Version)

The fuzzy vault is an error tolerant authentication method that ensures the privacy of the stored reference data. Several publications have proposed the application of the fuzzy vault to fingerprints, but the results of subsequent analyses indicate that a single finger does not contain sufficient information for a secure implementation. In this contribution, we present an implementation of a fuzzy vault based on minutiae information in several fingerprints aiming at a security level comparable to current cryptographic applications. We analyze and empirically evaluate the security, efficiency, and robustness of the construction and several optimizations. The results allow an assessment of the capacity of the scheme and an appropriate selection of parameters. Finally, we report on a practical simulation conducted with ten users.Comment: This article represents the full paper of a short version to appear in the Proceedings of BIOSIG 2010 (copyright of Gesellschaft f\"ur Informatik