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Fingerprints are the most widely used biometric feature for person identification andverification in the field of biometric identification. Fingerprints possess two main types offeatures that are used for automatic fingerprint identification and verification: (i) Ridgeand furrow structure that forms a special pattern in the central region of the fi...
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Biometric systems are an uprising technique of identification in today’s world. Many different systems have been used in everyone’s daily life in the past years, such as fingerprint, face scan, and others. We propose a new identification method using Elektrokardiogramms (EKGs) converted into a heatmap of a set of aligned R-peaks (heartbeats), formi...
The quest for efficient and reliable user authentication techniques motivated this research work. Undoubtedly, biometric technology has become a foundation of an extensive array of secured identification and personal verification solutions across the world. In fact, the technology has been widely adopted in the work environments and critical indust...
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In this work, authors propose a novel scheme with commutative encryption and reversible watermarking for a fingerprint image. Due to commutative property one can embed and extract watermark in either plain or encrypted fingerprint image, irrespective of order in which encryption or watermarking is applied. The encryption and the watermarking keys a...
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we started this paper to continue the trend of Fingerprint Biometric Authentication System by making it more secure, robust and flexible. Despite the vast research by the scientists in the field of improving the recognition of the fingerprints, little is known about the viewpoint of flexibility for authentication. We went through more than 30 paper...
Citations
... In 2007, Wang et all [16], they used Support Vector Machine (SVM) classifier to calculate singularity information and coefficients of the given orientation model, where the singular points and orientation patterns are used for fingerprints matching. In 2009, Kant and Nath [17], they extracted singular delta points from fingerprints then only single print of person used for comparison manner. In 2010, Sanjekar and Dhabe [18], introduced a modified approach by using Haar wavelet transformation to decompose the given fingerprint samples up to three levels then extracting wavelet statistical features from decomposed images, then use distance vector to find the proximity among the given dataset. ...
... While many researchers have focused on the classification of the fingerprint, Kant et al. presented an approach to accelerating the matching process and decreasing the processing time by classifying the fingerprint pattern into 12 J o u r n a l P r e -p r o o f different groups at the enrollment process of the fingerprint recognition system [52]. And they improved fingerprint matching while matching the input template with stored template. ...
Fingerprint plays a fundamental role in community security and criminal investigation, such as forensic investigation, law enforcement, customs access and public security organs. This can also help to provide a more enjoyable and secure life to people. Various machine learning and neural network approaches have been proposed for fingerprint acquisition, detection, classification, and analysis. In this survey, we present an up-to-date literature evaluation of fingerprint classification algorithms and fingerprint application in the area of criminal investigation. Firstly, we discuss the characteristics of fingerprint and the application in criminal investigation. In addition, we analyze and compare machine learning algorithms of fingerprint in terms of classification, matching, feature extraction, fingerprint and finger-vein recognition, and spoof detection. Finally, we highlight the challenges in the fingerprint analysis and discuss the future directions.
... Thus it is obvious there is a need for a solution which is able to cooperate with the current data speed. There are more possibilities how to decrease the transferred data size: it is possible to compressing, and converting the information, or to clasterizing into different sets of the templates by the main attribution, like the Henry classification 2 [5]. All in all that has to be highlighted, if we are using different type of biometric detectors -which is absolutely possible in a worldwide usage -we cannot encode the biometrical information into different datasets, because the interconnections are usually missing among these solutions. ...
... The other, more commonly used technique is the "one-to-many" identification which is looking for the "most similar" sample from the database to the captured image. Generally the verification is easier and quicker than the identification, because the latter one has to investigate many stored image and find the most suitable one [5]. ...
... In this section, the most related works illustrated based on fingerprint recognition and matching. In 2009, Kant and Nath [22], they used singular delta point to identifying the individual persons based on his fingerprint central point, which is also used to distinguish it from other samples. In 2010, Sanjekar and Dhabe [23], they used Haar wavelet for sampling fingerprint images into 3 levels to extract the statistical features from it, then distance measure used for comparison purpose. ...
Nowadays, authentication process in biometric system become most critical task with the expansive of individual information in the world. Where in many current applications, devices and commercial treatments required fingerprint identification process in order to verify the requested services. Most technologies also motivate to this direction. With the increasing of fingerprints uses, there is a need to provide a technique that able to handle the issues that exist in fingerprint acquisition and verification processes. Typically, fingerprint authenticated based on pick small amount of information from some points called Minutiae points. This approach suffers from many issues and provide poor results when the samples of fingerprints are degraded (scale, illumination, direction) changes. However, BRISK algorithm used to handle the previous issues and to extract the significant information from corner points in fingerprint. BRISK is invariant to scale, illumination, and direction changes and its able to pick large number of information when compared with minutiae points. In this paper, BRISK algorithm used based on image based approach, where current recognition matrices are developed and proposed new metrics without need for human interaction. UPEK dataset used to test the performance of proposed system, where the results show high accuracy rate in this dataset. Proposed system evaluated using FAR, FRR, EER and Accuracy and based on selected metrics the proposed system and methodology achieve high accuracy rate than others, and gives a novel modification in authentication task in biometric systems
... In 2007, Wang et all [16], they used Support Vector Machine (SVM) classifier to calculate singularity information and coefficients of the given orientation model, where the singular points and orientation patterns are used for fingerprints matching. In 2009, Kant and Nath [17], they extracted singular delta points from fingerprints then only single print of person used for comparison manner. In 2010, Sanjekar and Dhabe [18], introduced a modified approach by using Haar wavelet transformation to decompose the given fingerprint samples up to three levels then extracting wavelet statistical features from decomposed images, then use distance vector to find the proximity among the given dataset. ...
... Thus it is obvious there is a need for a solution which is able to cooperate with the current data speed. There are more possibilities how to decrease the transferred data size: it is possible to compressing, and converting the information, or to clasterizing into different sets of the templates by the main attribution, like the Henry classification 2 [5]. All in all that has to be highlighted, if we are using different type of biometric detectors -which is absolutely possible in a worldwide usage -we cannot encode the biometrical information into different datasets, because the interconnections are usually missing among these solutions. ...
... The other, more commonly used technique is the "one-to-many" identification which is looking for the "most similar" sample from the database to the captured image. Generally the verification is easier and quicker than the identification, because the latter one has to investigate many stored image and find the most suitable one [5]. ...
In the modern society, the rights and the limitations became a crucial part of the everyday life. While more and more PIN code, password and RFID tag spreads, the growth of the biometrical authentication is less pronounced. Certainly, the background of this phenomenon is complex, but the efficiency and robustness are significant determinants. The multimodal identification brings biometrics closer into the everyday life. Nevertheless, to break the limits an effective and adaptive controller algorithm is necessary. In this study, the adaptive neuro-fuzzy inference system has been revealed as a possible controller algorithm in a complex biometrical identification system, which simultaneously accomplishes fuzzy and neural attributions.
... Biometric systems based on single source of information are called unimodal systems. Although some unimodal systems [2] have got considerable improvement in reliability and accuracy, they often suffer from enrollment problems due to non-universal biometrics traits, susceptibility to biometric spoofing or insufficient accuracy caused by noisy data [3] Hence, single biometric may not be able to achieve the desired performance requirement in real world applications. One of the methods to overcome these problems is to make use of multimodal biometric authentication systems, which combine information from multiple modalities to arrive at a decision. ...
A biometric system is important to a pattern recognition system that operates by acquiring biometric data from an individual, extracting a feature set from the acquired data, and comparing this feature set against the template set in the database. Multimodal biometric systems are becoming more popular; Fingerprint recognition is the most popular physiological characteristic used to identify a person in biometric systems, because of feasibility, permanence, distinctiveness, reliability, accuracy, and acceptability Signature recognition is the most popular behavioral characteristic used in biometric systems. Thus, we believe that the combination of these two methods will have a reliable and accurate result. We propose a weighted fusion scheme, which transforms the scores into a common range, assigned weights and combines them, giving the final fused score.
... Biometric systems based on single source of information are called unimodal systems. Although some unimodal systems [2] have got considerable improvement in reliability and accuracy, they often suffer from enrollment problems due to non-universal biometrics traits, susceptibility to biometric spoofing or insufficient accuracy caused by noisy data [3] Hence, single biometric may not be able to achieve the desired performance requirement in real world applications. One of the methods to overcome these problems is to make use of multimodal biometric authentication systems, which combine information from multiple modalities to arrive at a decision. ...
InMulti-modal biometrics has attracted strong interest in recent years and in future. This paper approaches to multi-modal biometrics based on biometric source, the type of sensing used, and the depth of collaborative interaction in the processing. This paper also attempts to identify some of the challenges and issues that confront research in multimodal biometrics.
... Therefore, different features that come from behavioural aspects can be linked with different aspects of digital forensics for proper matching. Based on these methods Kant and Nath [5] extrapolates that features of different fingerprints may exhibit different characteristics. ...
Multimodal biometrics represents various categories of morphological and intrinsic aspects with two or more computerized biological characteristics such as facial structure, retina, keystrokes dynamics, voice print, retinal scans, and patterns for iris, facial recognition, vein structure, scent, hand geometry, and signature recognition. The objectives of Digital Forensics (DF), on the other hand, is to inspect digital media in a forensically sound manner with the essence of identifying, discovering, recovering, analysing the artifacts and presenting facts and suggestions about the discovered information to any court of law or civil proceedings. Because the accuracy of biometric indicators may rarely be investigated during a digital forensic investigation processes, integrating digital forensics with multimodal biometrics can enable effective digital forensic investigations on multiple captured physiological and behavioural characteristics.
This paper, therefore, presents a self-adaptive approach for integrating digital forensics with multimodal biometrics. This is motivated by the fact that, as of the time of writing this paper, there is lack of effective and standardised methods for performing digital investigation across multimodal biometric indicators. In addition, there are also no proper digital forensic biometric management strategies in place. For this reason, to enable effective digital investigations on multiple captured physiological and behavioural characteristics, this paper aims at proposing a framework that is meant to facilitate the integration of DF and multimodal biometrics. The framework is also meant to enhance the analysis of potential digital evidence during investigations. Integrating multimodal biometrics and digital forensics using the proposed framework gives a promising approach to add value especially in enforcing security measures in different systems as well as a restricting factor to unauthorized access key discoveries. The integration of digital forensics with multimodal biometrics is the main focus of this paper.
... [t is time consuming and confusing for similar class. Singular delta points are extracted in [11] using Henry Classification. [n this method, only a single print of a person is used and has time complexity. ...
Fingerprint recognition refers to the automated method of verifying a match between two human fingerprints which is used to identify individuals and verify their identity. A fingerprint sensor is used to capture a digital image of the fingerprint pattern. The captured image is digitally processed to create a biometric template (a collection of extracted features) which is stored and used for matching. In this paper, we investigate a fingerprint recognition approach by local robust features extraction and matching. In this approach, first the local features are extracted using Speeded-Up Robust Feature (SURF) algorithm. Then the features of the test fingerprint image are compared against two or more exiting template image features for matching. The matching method uses a matching threshold. Two features match when the distance between them is less than the matching threshold. It also eliminates ambiguous matches in addition to using the matching threshold. Finally it calculates the similarity index/matching score from the matching points and take the decision on matching. Since SURF is a scale and rotation invariant algorithm, the fingerprint recognition system shows better recognition accuracy in presence of rotation, scaling and partial distortion of the test image. The experimental results indicate its effectiveness and improved performance over the state of the art.
