1,524 research outputs found
Multispectral Palmprint Encoding and Recognition
Palmprints are emerging as a new entity in multi-modal biometrics for human
identification and verification. Multispectral palmprint images captured in the
visible and infrared spectrum not only contain the wrinkles and ridge structure
of a palm, but also the underlying pattern of veins; making them a highly
discriminating biometric identifier. In this paper, we propose a feature
encoding scheme for robust and highly accurate representation and matching of
multispectral palmprints. To facilitate compact storage of the feature, we
design a binary hash table structure that allows for efficient matching in
large databases. Comprehensive experiments for both identification and
verification scenarios are performed on two public datasets -- one captured
with a contact-based sensor (PolyU dataset), and the other with a contact-free
sensor (CASIA dataset). Recognition results in various experimental setups show
that the proposed method consistently outperforms existing state-of-the-art
methods. Error rates achieved by our method (0.003% on PolyU and 0.2% on CASIA)
are the lowest reported in literature on both dataset and clearly indicate the
viability of palmprint as a reliable and promising biometric. All source codes
are publicly available.Comment: Preliminary version of this manuscript was published in ICCV 2011. Z.
Khan A. Mian and Y. Hu, "Contour Code: Robust and Efficient Multispectral
Palmprint Encoding for Human Recognition", International Conference on
Computer Vision, 2011. MATLAB Code available:
https://sites.google.com/site/zohaibnet/Home/code
Fingerprint Recognition Using Translation Invariant Scattering Network
Fingerprint recognition has drawn a lot of attention during last decades.
Different features and algorithms have been used for fingerprint recognition in
the past. In this paper, a powerful image representation called scattering
transform/network, is used for recognition. Scattering network is a
convolutional network where its architecture and filters are predefined wavelet
transforms. The first layer of scattering representation is similar to sift
descriptors and the higher layers capture higher frequency content of the
signal. After extraction of scattering features, their dimensionality is
reduced by applying principal component analysis (PCA). At the end, multi-class
SVM is used to perform template matching for the recognition task. The proposed
scheme is tested on a well-known fingerprint database and has shown promising
results with the best accuracy rate of 98\%.Comment: IEEE Signal Processing in Medicine and Biology Symposium, 201
Fingerprint Verification based on Gabor Filter Enhancement
Human fingerprints are reliable characteristics for personnel identification
as it is unique and persistence. A fingerprint pattern consists of ridges,
valleys and minutiae. In this paper we propose Fingerprint Verification based
on Gabor Filter Enhancement (FVGFE) algorithm for minutiae feature extraction
and post processing based on 9 pixel neighborhood. A global feature extraction
and fingerprints enhancement are based on Hong enhancement method which is
simultaneously able to extract local ridge orientation and ridge frequency. It
is observed that the Sensitivity and Specificity values are better compared to
the existing algorithms.Comment: 7 pages IEEE format, International Journal of Computer Science and
Information Security, IJCSIS November 2009, ISSN 1947 5500,
http://sites.google.com/site/ijcsis
Fingerprint Orientation Refinement Through Iterative Smoothing
We propose a new gradient-based method for the extraction of the orientation field associated to a
fingerprint, and a regularisation procedure to improve the orientation field computed from noisy
fingerprint images. The regularisation algorithm is based on three new integral operators, introduced and
discussed in this paper. A pre-processing technique is also proposed to achieve better performances of the
algorithm. The results of a numerical experiment are reported to give an evidence of the efficiency of the
proposed algorithm
Development Of Fingerprint Biometric Attendance Management System Using Wireless Connectivity
In this paper, we propose an integrated biometric access system for attendance management based on fingerprint identification and authentication for restricted area using wireless connectivity. Maintaining the attendance record in institutions, companies and organisations is an imperative factor, maintaining so manually is herculean task. Along with it, institutions with single machine and more crowd makes this work more complicated to make this easier, an efficient Biometric Fingerprint Attendance Management system is proposed. This system registers the user and accepts biometric input through use of mobile network, and all records will be saved for subsequent operations. Since input image is accepted through mobile, it provides greater portability and reduces need for any specific biometric hardware, which in turn reduces the hardware cost. It further provides and facilities to calculate and generate monthly report of attendance in order to reduce any human errors during calculations. Thus, the proposed system will help to improve the productivity of any organization if properly implemented.
DOI: 10.17762/ijritcc2321-8169.150315
Finger Vein Template Protection with Directional Bloom Filter
Biometrics has become a widely accepted solution for secure user authentication. However, the use of biometric traits raises serious concerns about the protection of personal data and privacy. Traditional biometric systems are vulnerable to attacks due to the storage of original biometric data in the system. Because biometric data cannot be changed once it has been compromised, the use of a biometric system is limited by the security of its template. To protect biometric templates, this paper proposes the use of directional bloom filters as a cancellable biometric approach to transform the biometric data into a non-invertible template for user authentication purposes. Recently, Bloom filter has been used for template protection due to its efficiency with small template size, alignment invariance, and irreversibility. Directional Bloom Filter improves on the original bloom filter. It generates hash vectors with directional subblocks rather than only a single-column subblock in the original bloom filter. Besides, we make use of multiple fingers to generate a biometric template, which is termed multi-instance biometrics. It helps to improve the performance of the method by providing more information through the use of multiple fingers. The proposed method is tested on three public datasets and achieves an equal error rate (EER) as low as 5.28% in the stolen or constant key scenario. Analysis shows that the proposed method meets the four properties of biometric template protection. Doi: 10.28991/HIJ-2023-04-02-013 Full Text: PD
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