1,246 research outputs found
A review of finger vein recognition system
Recently, the security-based system using finger vein as a biometric trait has been getting more attention from researchers all over the world, and these researchers have achieved positive progress. Many works have been done in different methods to improve the performance and accuracy of the personal identification and verification results. This paper discusses the previous methods of finger vein recognition system which include three main stages: preprocessing, feature extraction and classification. The advantages and limitations of these previous methods are reviewed at the same time we present the main problems of the finger vein recognition system to make it as a future direction in this field
Generic multimodal biometric fusion
Biometric systems utilize physiological or behavioral traits to automatically identify individuals. A unimodal biometric system utilizes only one source of biometric information and suffers from a variety of problems such as noisy data, intra-class variations, restricted degrees of freedom, non-universality, spoof attacks and unacceptable error rates. Multimodal biometrics refers to a system which utilizes multiple biometric information sources and can overcome some of the limitation of unimodal system. Biometric information can be combined at 4 different levels: (i) Raw data level; (ii) Feature level; (iii) Match-score level; and (iv) Decision level. Match score fusion and decision fusion have received significant attention due to convenient information representation and raw data fusion is extremely challenging due to large diversity of representation. Feature level fusion provides a good trade-off between fusion complexity and loss of information due to subsequent processing. This work presents generic feature information fusion techniques for fusion of most of the commonly used feature representation schemes. A novel concept of Local Distance Kernels is introduced to transform the available information into an arbitrary common distance space where they can be easily fused together. Also, a new dynamic learnable noise removal scheme based on thresholding is used to remove shot noise in the distance vectors. Finally we propose the use of AdaBoost and Support Vector Machines for learning the fusion rules to obtain highly reliable final matching scores from the transformed local distance vectors. The integration of the proposed methods leads to large performance improvement over match-score or decision level fusion
Finger Vein Recognition Based on a Personalized Best Bit Map
Finger vein patterns have recently been recognized as an effective biometric identifier. In this paper, we propose a finger vein recognition method based on a personalized best bit map (PBBM). Our method is rooted in a local binary pattern based method and then inclined to use the best bits only for matching. We first present the concept of PBBM and the generating algorithm. Then we propose the finger vein recognition framework, which consists of preprocessing, feature extraction, and matching. Finally, we design extensive experiments to evaluate the effectiveness of our proposal. Experimental results show that PBBM achieves not only better performance, but also high robustness and reliability. In addition, PBBM can be used as a general framework for binary pattern based recognition
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
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