International Journal of Computer and Applications

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Handbook Of Biometrics

Feature-level fusion of fingerprint and finger-vein for personal identification

Biometric systems are increasingly replacing traditional password- and token-based authentication systems. Security and recognition accuracy are the two most important aspects to consider in designing a biometric system. In this paper, a comprehensive review is presented to shed light on the latest developments in the study of fingerprint-based biometrics covering these two aspects with a view to improving system security and recognition accuracy. Based on a thorough analysis and discussion, limitations of existing research work are outlined and suggestions for future work are provided. It is shown in the paper that researchers continue to face challenges in tackling the two most critical attacks to biometric systems, namely, attacks to the user interface and template databases. How to design proper countermeasures to thwart these attacks, thereby providing strong security and yet at the same time maintaining high recognition accuracy, is a hot research topic currently, as well as in the foreseeable future. Moreover, recognition accuracy under non-ideal conditions is more likely to be unsatisfactory and thus needs particular attention in biometric system design. Related challenges and current research trends are also outlined in this paper.

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Security and Accuracy of Fingerprint-Based Biometrics: A Review

The use of mobile smart devices for storing sensitive information and accessing online services is increasing. At the same time, methods for authenticating users into their devices and online services that are not only secure, but also privacy and user-friendly are needed. In this paper, we present our initial explorations of the use of lock pattern dynamics as a secure and user-friendly two-factor authentication method. We developed an application for the Android mobile platform to collect data on the way individuals draw lock patterns on a touchscreen. Using a Random Forest machine learning classifier this method achieves an average Equal Error Rate (EER) of approximately 10.39%, meaning that lock patterns biometrics can be used for identifying users towards their device, but could also pose a threat to privacy if the users’ biometric information is handled outside their control.

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Exploring Touch-Screen Biometrics for User Identification on Smart Phones

In today’s world, where terrorist attacks are on the rise, employment of infallible security systems is a must. Iris recognition enjoys universality, high degree of uniqueness and moderate user co-operation. This makes Iris recognition systems unavoidable in emerging security & authentication mechanisms. We propose an iris recognition system based on vector quantization. The proposed system does not need any pre-processing and segmentation of the iris. We have tested LBG, Kekre’s Proportionate Error Algorithm (KPE) & Kekre’s Fast Codebook Generation Algorithm (KFCG) for the clustering purpose. From the results it is observed that KFCG requires 99.79% less computations as that of LBG and KPE. Further the KFCG method gives best performance with the accuracy of 89.10% outperforming LBG that gives accuracy around 81.25%. Performance of individual methods is evaluated and presented in this paper.

Iris Recognition Using Vector Quantization

Speaker identification system is one of the applications of biometric using voice signal. In this paper we have implemented a speaker recognition system using a combination of Mel Frequency Capestral Coefficients (MFCC) & Kekre's Median Codebook Generation Algorithm (KMCG). The MFCC algorithm is used for feature extraction while the KMCG algorithm plays important role in code book generation and feature matching. For implementation simplicity the system is built as a text dependent system, i.e. common text used by all users. KMCG algorithm provides implementation simplicity along with high level of accuracy.

Speaker recognition using Vector Quantization by MFCC and KMCG clustering algorithm

Finger-knuckle-print is one of the emerging biometric traits. The region of interest is the area where the maximum information is centered, for a finger knuckle it is the area surrounding the knuckle region. A good system needs this region of interest as input for the feature vector extraction. In this method we present a novel approach for segmentation of Region of interest (ROI) of a finger-knuckle-print using gradient field orientation & its local field strength. This approach is fast and gives good results in case of shift in finger-knuckle-placement (translational shift).

Finger-Knuckle-Print Region of Interest Segmentation Using Gradient Field Orientation & Coherence

Iris recognition enjoys universality, high degree of uniqueness and moderate user co-operation. This makes iris recognition systems unavoidable in emerging security & authentication mechanisms. An iris recognition system based on vector quantization (VQ) techniques is proposed and its performance is compared with the discrete cosine transform (DCT). The proposed system does not need any pre-processing and segmentation of the iris. We have tested Linde-Buzo- Gray (LBG), Kekre's proportionate error (KPE) algorithm and Kekre's fast codebook generation (KFCG) algorithm for the clustering purpose. Proposed vector quantization based method using KFCG requires 99.99% less computations as that of full 2-dimensional DCT. Further, the KFCG method gives better performance with the accuracy of 89.10% outperforming DCT that gives accuracy around 66.10%.

Performance Comparison of DCT and VQ Based Techniques for Iris Recognition

Fingerprint recognition is a widely used biometricidentification mechanism. In case of correlation based fingerprintrecognition detection of a consistent registration point is a crucialissue; this point can be a core point of a fingerprint. Manytechniques have been proposed but success rate is highlydependent on input used and accurate core point detection is stillan open issue. Here we discuss a core point detection algorithmwhich is computationally simple and gives consistent detection ofa core point. The proposed technique is based on orientation fieldof the fingerprint which is calculated using gradient basedtechnique and optimized neighborhood averaging to generate asmoother orientation field, on which we operate a speciallydesigned mask for detecting core point as the orientation field inthe region of the core point is different than the other area.Though all fingerprints don’t possess core point still thisalgorithm is useful to detect high curvature regions. Thisalgorithm is helpful in the development of correlation basedAutomatic Fingerprint Recognition System (AFIS).

Vinayak Ashok Bharadi

Papers

Iris Recognition Using Vector Quantization

Speaker recognition using Vector Quantization by MFCC and KMCG clustering algorithm

Finger-Knuckle-Print Region of Interest Segmentation Using Gradient Field Orientation & Coherence

Performance Comparison of DCT and VQ Based Techniques for Iris Recognition

Fingerprint's Core Point Detection Using Orientation Field