Human Verification using Multiple Fingerprint Texture Matchers (original) (raw)
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Human Verification using Multiple Fingerprint Texture
This paper presents a multimodal biometric verification system using multiple fingerprint matchers. The proposed verification system is based on multiple fingerprint matchers using Spatial Grey Level Dependence Method and Filterbank-based technique. The method independently extract fingerprint texture features to generate matching scores. These individual normalized scores are combined into a final score by the sum rule and the final score is eventually used to effect verification of a person as genuine or an imposter. The matching scores are used in two ways: in first case equal weights are assigned to each matching scores and in second case user specific weights are used. The proposed verification system has been tested on fingerprint database of FVC2002. The experimental results demonstrate that the proposed fusion strategy improves the overall accuracy of the system by reducing the total error rate of the system.
Fingerprint verification in multimodal biometrics
2006 IEEE Region 5 Conference, 2006
This paper presents the development of fingerprint biometrics verification and vetting management system for Sensitive Organization. The idea behind this study is to improve security in sensitive institutions through integration of fingerprint biometrics into identity database. A fingerprint is a very recognized and acceptable security feature. It is traditionally used for human identification and criminal vetting of newly recruited security staff. There was need to develop Human Identity Authentication System that verify the true identity of people gaining entry into sensitive institution as an additional security layer to existing traditional techniques (National Identification Card, Driving license and passport). Traditional Techniques of human identity verification suffer from security Vulnerabilities such as masquerading identity; mobility issues (include lost, forgery and misplacement) and inaccuracy. The study was conducted using visual studio 2010 on DotNet framework 4.0 with C# object oriented programming language. The backend database used was MySQL relational database management system (RDMBS). The research produced a number of key results include the development of biometric security layer that is able to identify and verify identity of an individual using enrolled fingerprint template. Other results include the ability to capture military police security data, storage, retrieval and dissemination. The developed application performance was evaluated by enrolled ten fingerprints and captured related individual personal information. The result indicated 99.999% biometric accuracy levels attained with error allowance of 0.001% False Acceptance Rate (FAR) and 0.001% False Rejection Rate (FRR). In conclusion, the study shows that the integration of fingerprint biometric system in sensitive institution database can improve security of the organization and alleviate problem associated with traditional identity verification techniques.
Fingerprint Verification Using the Texture of Fingerprint Image
2009 Second International …, 2009
In this paper, a new fingerprint verification algorithm is presented that improves matching accuracy by overcoming the shortcomings of previous methods due to poor image quality. It reduces multi-spectral noise by enhancing a fingerprint image to accurately and reliably determine a reference point using the orientation reliability and then extract a 129 X 129 block, making the reference point its center. From the 16 co-occurrence matrices, four statistical descriptors are computed. The experimental results have been analyzed using FVC testing protocol; the equal error rate (EER) is 0.32%. Furthermore, the comparison with other methods shows that the proposed method is more accurate and robust for reliable fingerprint verification.
A multimodal authentication for biometric verification system using palmprints and fingers
2019
Trusted identification approaches play a critical role in everyday life and daily activities of humans. Hand as a physiological characteristic that has high acceptability and stability that contains several biometrics components has attracted the attention of many scholars so that almost all parts of it are considered as a member of the biometric system. The proposed method in this research is verification by a color image of the palm of the hand and the index, middle, ring and little fingers which is implemented by a new method for extracting texture features on images of 177 individuals from the Hong Kong Polytechnic University ContactFree 3D / 2D Hand Images Database. The proposed feature extraction method is the use of Turn Counts in each of Gabor's filters applied in different directions and scales on each of the RGB components of the images individually. Before classification, a binary genetic algorithm is applied to use the best combination of features for each color comp...
This article is an overview of a current multimodal biometrics research based on fingerprint and palm-print. It explains the pervious study for each modal separately and its fusion technique with another biometric modal. The basic biometric system consists of four stages: firstly, the sensor which is used for enrolment & recognition the biometrics data. Secondly, the pre-processing stage which includes the enhancement and segmentation of Region-Of-Interest ROI. Thirdly, features extracted from the output of the pre-processing and each modal of biometrics having different type of features. Fourthly, the matching stage is to compare the acquired feature with the template in the database. Finally, the database which stores the features for the matching stags. Multimodal is being gathered of various types of biometrics objects from the same human. In this paper, the biometric system gives an explanation for each model. Also, the modalities of biometrics are discussed as well as focused on two different modalities : fingerprint and Palm-Print. Abstract-This article is an overview of a current multimodal biometrics research based on fingerprint and palm-print. It explains the pervious study for each modal separately and its fusion technique with another biometric modal. The basic biometric system consists of four stages: firstly, the sensor which is used for enrolment & recognition the biometrics data. Secondly, the pre-processing stage which includes the enhancement and segmentation of Region-Of-Interest ROI. Thirdly, features extracted from the output of the pre-processing and each modal of biometrics having different type of features. Fourthly, the matching stage is to compare the acquired feature with the template in the database. Finally, the database which stores the features for the matching stags. Multimodal is being gathered of various types of biometrics objects from the same human. In this paper, the biometric system gives an explanation for each model. Also, the modalities of biometrics are discussed as well as focused on two different modalities: fingerprint and Palm-Print.
Automated Biometric Verification: A Survey on Multimodal Biometrics
In the world of computer science & Information Technology security is essential and important issue. Identification and Authentication Techniques plays an important role while dealing with security and integrity. The human physical characteristics like fingerprints, face, hand geometry, voice and iris are known as biometrics. These features are used to provide an authentication for computer based security systems. Biometric verification refers to an automatic verification of a person based on some specific biometric features derived from his/her physiological and/or behavioral characteristics. Biometrics is the science and technology of measuring and analyzing biological data of human body, extracting a feature set from the acquired data, and comparing this set against to the template set in the database. The future in biometrics seems to belong to the multimodal biometrics (a biometric system using more than one biometric feature) as a Unimodal biometric system (biometric system using single biometric feature) has to contend with a number of problems. In this paper, a survey of some of the multimodal biometrics is conducted.
Person Identification using Multiple Fingerprint Matching
2020
In biometric security system still fingerprint authentication is a challenging task for the altered and compressed images. Apart from the Automatic Fingerprint Identification System(AFIS), the altered, blurred and compressed images are still having quality issues. This paper presents an efficient multi-model biometric system based on multiple fingerprint images which includes altered fingerprint images also. The system utilizes fingerprint scanner to simultaneously collect fingerprints of multiple fingers on a hand in one image. The collected multi-finger images are first segmented to get individual fingers. Quality of each individual finger is analysed and its minutiae points are extracted. The minutiae points of each finger is extracted from multiple fingerprint images and compared with the corresponding individual finger of the input
A Novel and Efficient Algorithm of Textural Feature Extraction for Fingerprint Identification
2012
With the need of automatic personal identification and their extensive use in forensics, fingerprints are receiving a lot of attention. However numerous fingerprint systems currently available still do not meet performance requirement of several civilian applications as they use traditional approach for fingerprint recognition. There is a major disadvantage of traditional approach, as they do not actually utilize the rich discriminatory information contained in fingerprint. Thus trying to eliminate this shortcoming we present an improved & efficient approach for fingerprint recognition providing accurate automatic personal identification. Here we extract the local ridge features of fingerprint image by a bank of 16 Gabor filters divided in squared blocks. The matching is based on Euclidian distance between Finger codes. Hence this approach make use of orientation and frequency of local ridge structure which is the rich discriminatory information in fingerprint, secondly square tessellation covers the entire image and Euclidian distance based matching of finger codes increases the speed of matching process.
Indonesian Journal of Electrical Engineering and Computer Science
In this paper, we present a multimodal biometric recognition system that combines fingerprint, fingervein and face images based on cascade advanced and decision level fusion. First, in fingerprint recognition system, the images are enhanced using gabor filter, binarized and passed to thinning method. Then, the minutiae points are extracted to identify that an individual is genuine or impostor. In fingervein recognition system, image processing is required using Linear Regression Line, Canny and local histogram equalization technique to improve better the quality of images. Next, the features are obtained using Histogram of Oriented Gradient (HOG). Moreover, the Convolutional Neural Networks (CNN) and the Local Binary Pattern (LBP) are applied to detect and extract the features of the face images, respectively. In addition, we proposed three different modes in our work. At the first, the person is identified when the recognition system of one single biometric modality is matched. At ...