Impact of Noisy Singular Point Detection on Performance of Fingerprint Matching

The performance of fingerprint matching has significantly improved in the recent times. However, this performance is still affected by many factors such as inadequate detection of singular points, poor-quality and noisy fingerprint images mostly result in spurious or missing singular points, which generally results in degradation of the overall performance of the fingerprint matching.   This paper presents the impact of noisy or spurious singular (core/delta) points on the performance of fingerprint matching. The algorithm comprises of image enhancement stage, the singular points extraction stage and post-processing stage. The image enhancement stage preprocessed the fingerprint images, the singular point extraction stage extracts the true and the noisy or false singular points, while the post processing stage eliminate the spurious singular point.  Benchmarked FVC2000, FVC2002, FVC2004 and FVC2006 fingerprint databases which comprise four datasets each were used for the experimental study. The completion time for the singular point extraction on each dataset were computed. The matching algorithm was also implemented to verify the impact of noisy singular points on false non match rate (FNMR), false match rate (FMR) and matching speed. The completion time extraction of singular points from the noisy fingerprint images is 263seconds whereas the completion time for extraction of true singular points is 82seconds. The increase in completion time is due to the inclusion of spurious features (noise/contaminants), whereas there is time decreases after the spurious features had been eliminated.  The obtained values and analysis revealed that poor and noisy quality fingerprint images have adverse effect on the performance of fingerprint matching. &nbsp

A Review of Audio Features and Statistical Models Exploited for Voice Pattern Design

Audio fingerprinting, also named as audio hashing, has been well-known as a powerful technique to perform audio identification and synchronization. It basically involves two major steps: fingerprint (voice pattern) design and matching search. While the first step concerns the derivation of a robust and compact audio signature, the second step usually requires knowledge about database and quick-search algorithms. Though this technique offers a wide range of real-world applications, to the best of the authors' knowledge, a comprehensive survey of existing algorithms appeared more than eight years ago. Thus, in this paper, we present a more up-to-date review and, for emphasizing on the audio signal processing aspect, we focus our state-of-the-art survey on the fingerprint design step for which various audio features and their tractable statistical models are discussed.Comment: http://www.iaria.org/conferences2015/PATTERNS15.html ; Seventh International Conferences on Pervasive Patterns and Applications (PATTERNS 2015), Mar 2015, Nice, Franc

A survey of fingerprint classification Part I: taxonomies on feature extraction methods and learning models

This paper reviews the fingerprint classification literature looking at the problem from a double perspective. We first deal with feature extraction methods, including the different models considered for singular point detection and for orientation map extraction. Then, we focus on the different learning models considered to build the classifiers used to label new fingerprints. Taxonomies and classifications for the feature extraction, singular point detection, orientation extraction and learning methods are presented. A critical view of the existing literature have led us to present a discussion on the existing methods and their drawbacks such as difficulty in their reimplementation, lack of details or major differences in their evaluations procedures. On this account, an experimental analysis of the most relevant methods is carried out in the second part of this paper, and a new method based on their combination is presented.This work was supported by the Research Projects CAB(CDTI), TIN2011-28488, and TIN2013-40765-P.

A Survey of Fingerprint Classification Part I: Taxonomies on Feature Extraction Methods and Learning Models

This paper reviews the fingerprint classification literature looking at the problem from a double perspective. We first deal with feature extraction methods, including the different models considered for singular point detection and for orientation map extraction. Then, we focus on the different learning models considered to build the classifiers used to label new fingerprints. Taxonomies and classifications for the feature extraction, singular point detection, orientation extraction and learning methods are presented. A critical view of the existing literature have led us to present a discussion on the existing methods and their drawbacks such as difficulty in their reimplementation, lack of details or major differences in their evaluations procedures. On this account, an experimental analysis of the most relevant methods is carried out in the second part of this paper, and a new method based on their combination is presented.Research Projects CAB(CDTI) TIN2011-28488 TIN2013-40765Spanish Government FPU12/0490

Gender Estimation from Fingerprints Using DWT and Entropy

Gender estimation from fingerprints have wide range of applications, especially in the field of forensics where identifying the gender of a criminal can reduce the list of suspects significantly. Although there have been quite a few research papers in the field of gender estimation from fingerprints most of those experiments used a lot of features but were only able to achieve poor classification results. That being the motivation behind the study we successfully proposed two different approaches for gender estimation from fingerprints and achieved high classification accuracy.;In this study we have developed two different approaches for gender estimation from fingerprints. The dataset used consists of 498 fingerprints of which 260 are male and 238 are female fingerprints. The first approach is based on wavelet analysis and uses features obtained from a six level discrete wavelet transform (DWT). Classification is performed using a decision stump classifier implemented in weka and was able to achieve a classification accuracy of 95.38% using the DWT approach. The second approach uses wavelet packet analysis and extracted the Shannon entropy and log-energy entropy from the coefficients of wavelet packet transform and provided a classification accuracy of 96.59% on the same dataset using decision stump classifier implemented in weka

A Study on Automatic Latent Fingerprint Identification System

Latent fingerprints are the unintentional impressions found at the crime scenes and are considered crucial evidence in criminal identification. Law enforcement and forensic agencies have been using latent fingerprints as testimony in courts. However, since the latent fingerprints are accidentally leftover on different surfaces, the lifted prints look inferior. Therefore, a tremendous amount of research is being carried out in automatic latent fingerprint identification to improve the overall fingerprint recognition performance. As a result, there is an ever-growing demand to develop reliable and robust systems. In this regard, we present a comprehensive literature review of the existing methods utilized in latent fingerprint acquisition, segmentation, quality assessment, enhancement, feature extraction, and matching steps. Later, we provide insight into different benchmark latent datasets available to perform research in this area. Our study highlights various research challenges and gaps by performing detailed analysis on the existing state-of-the-art segmentation, enhancement, extraction, and matching approaches to strengthen the research

Semi-supervised cross-entropy clustering with information bottleneck constraint

In this paper, we propose a semi-supervised clustering method, CEC-IB, that models data with a set of Gaussian distributions and that retrieves clusters based on a partial labeling provided by the user (partition-level side information). By combining the ideas from cross-entropy clustering (CEC) with those from the information bottleneck method (IB), our method trades between three conflicting goals: the accuracy with which the data set is modeled, the simplicity of the model, and the consistency of the clustering with side information. Experiments demonstrate that CEC-IB has a performance comparable to Gaussian mixture models (GMM) in a classical semi-supervised scenario, but is faster, more robust to noisy labels, automatically determines the optimal number of clusters, and performs well when not all classes are present in the side information. Moreover, in contrast to other semi-supervised models, it can be successfully applied in discovering natural subgroups if the partition-level side information is derived from the top levels of a hierarchical clustering

An Efficient Fingerprint Identification using Neural Network and BAT Algorithm

The uniqueness, firmness, public recognition, and its minimum risk of intrusion made fingerprint is an expansively used personal authentication metrics. Fingerprint technology is a biometric technique used to distinguish persons based on their physical traits. Fingerprint based authentication schemes are becoming increasingly common and usage of these in fingerprint security schemes, made an objective to the attackers. The repute of the fingerprint image controls the sturdiness of a fingerprint authentication system. We intend for an effective method for fingerprint classification with the help of soft computing methods. The proposed classification scheme is classified into three phases. The first phase is preprocessing in which the fingerprint images are enhanced by employing median filters. After noise removal histogram equalization is achieved for augmenting the images. The second stage is the feature Extraction phase in which numerous image features such as Area, SURF, holo entropy, and SIFT features are extracted. The final phase is classification using hybrid Neural for classification of fingerprint as fake or original. The neural network is unified with BAT algorithm for optimizing the weight factor

Indexing techniques for fingerprint and iris databases

This thesis addresses the problem of biometric indexing in the context of fingerprint and iris databases. In large scale authentication system, the goal is to determine the identity of a subject from a large set of identities. Indexing is a technique to reduce the number of candidate identities to be considered by the identification algorithm. The fingerprint indexing technique (for closed set identification) proposed in this thesis is based on a combination of minutiae and ridge features. Experiments conducted on the FVC2002 and FVC2004 databases indicate that the inclusion of ridge features aids in enhancing indexing performance. The thesis also proposes three techniques for iris indexing (for closed set identification). The first technique is based on iriscodes. The second technique utilizes local binary patterns in the iris texture. The third technique analyzes the iris texture based on a pixel-level difference histogram. The ability to perform indexing at the texture level avoids the computational complexity involved in encoding and is, therefore, more attractive for iris indexing. Experiments on the CASIA 3.0 database suggest the potential of these schemes to index large-scale iris databases

A Review: Person Identification using Retinal Fundus Images

In this paper a review on biometric person identification has been discussed using features from retinal fundus image. Retina recognition is claimed to be the best person identification method among the biometric recognition systems as the retina is practically impossible to forge. It is found to be most stable, reliable and most secure among all other biometric systems. Retina inherits the property of uniqueness and stability. The features used in the recognition process are either blood vessel features or non-blood vessel features. But the vascular pattern is the most prominent feature utilized by most of the researchers for retina based person identification. Processes involved in this authentication system include pre-processing, feature extraction and feature matching. Bifurcation and crossover points are widely used features among the blood vessel features. Non-blood vessel features include luminance, contrast, and corner points etc. This paper summarizes and compares the different retina based authentication system. Researchers have used publicly available databases such as DRIVE, STARE, VARIA, RIDB, ARIA, AFIO, DRIDB, and SiMES for testing their methods. Various quantitative measures such as accuracy, recognition rate, false rejection rate, false acceptance rate, and equal error rate are used to evaluate the performance of different algorithms. DRIVE database provides 100\% recognition for most of the methods. Rest of the database the accuracy of recognition is more than 90\%