Multimodal biometric authentication based on the fusion of finger vein and finger geometry
01 Sep 2009-Optical Engineering (International Society for Optics and Photonics)-Vol. 48, Iss: 9, pp 090501
TL;DR: This research shows three novelties compared to previous works, including the first approach to combine the finger vein and finger geometry information at the same time, and shows that recognition accuracy is significantly enhanced using the proposed method.
Abstract: We propose a new multimodal biometric recognition based on the fusion of finger vein and finger geometry. This research shows three novelties compared to previous works. First, this is the first approach to combine the finger vein and finger geometry information at the same time. Second, the proposed method includes a new finger geometry recognition based on the sequential deviation values of finger thickness extracted from a single finger. Third, we integrate finger vein and finger geometry by a score-level fusion method based on a support vector machine. Results show that recognition accuracy is significantly enhanced using the proposed method.
Citations
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TL;DR: A new finger vein recognition algorithm based on Band Limited Phase Only Correlation (BLPOC) and a new type of geometrical features called Width-Centroid Contour Distance (WCCD) which can improve the accuracy of finger geometry recognition.
Abstract: A new finger vein recognition algorithm based on Band Limited Phase Only Correlation.Finger width and Centroid Contour Distance for finger geometry recognition.The fusion of vein and geometry for a finger based bimodal biometrics system.A new infrared finger image database is made publicly available on the web. In this paper, a new approach of multimodal finger biometrics based on the fusion of finger vein and finger geometry recognition is presented. In the proposed method, Band Limited Phase Only Correlation (BLPOC) is utilized to measure the similarity of finger vein images. Unlike previous methods, BLPOC is resilient to noise, occlusions and rescaling factors; thus can enhance the performance of finger vein recognition. As for finger geometry recognition, a new type of geometrical features called Width-Centroid Contour Distance (WCCD) is proposed. This WCCD combines the finger width with Centroid Contour Distance (CCD). As compared with the single type of feature, the fusion of W and CCD can improve the accuracy of finger geometry recognition. Finally, we integrate the finger vein and finger geometry recognitions by a score-level fusion method based on the weighted SUM rule. Experimental evaluation using our own database which was collected from 123 volunteers resulted in an efficient recognition performance where the equal error rate (EER) was 1.78% with a total processing time of 24.22ms.
235 citations
TL;DR: This research proposes a new finger vein image restoration method to deal with skin scattering and optical blurring, and shows that the equal error rate (EER) of finger vein recognition with restoration was reduced by as much as 3.14% compared to the EER without restoration.
95 citations
TL;DR: Performance of the proposed personal authentication system is found to be better than the various existing systems.
94 citations
Cites methods from "Multimodal biometric authentication..."
...Due to finger pose variations, employing a soft biometric trait like the width of phalangeal joint [12] or finger geometry [16] is also not of much use for matching....
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TL;DR: This research proposes a new identification method of finger vascular patterns using a weighted local binary pattern (LBP) and support vector machine (SVM) and shows that the equal error rate (EER) is significantly lower compared to that without the proposed method or using a conventional method.
Abstract: Finger vein recognition is a biometric technique which identifies individuals using their unique finger vein patterns. It is reported to have a high accuracy and rapid processing speed. In addition, it is impossible to steal a vein pattern located inside the finger. We propose a new identification method of finger vascular patterns using a weighted local binary pattern (LBP) and support vector machine (SVM). This research is novel in the following three ways. First, holistic codes are extracted through the LBP method without using a vein detection procedure. This reduces the processing time and the complexities in detecting finger vein patterns. Second, we classify the local areas from which the LBP codes are extracted into three categories based on the SVM classifier: local areas that include a large amount (LA), a medium amount (MA), and a small amount (SA) of vein patterns. Third, different weights are assigned to the extracted LBP code according to the local area type (LA, MA, and SA) from which the LBP codes were extracted. The optimal weights are determined empirically in terms of the accuracy of the finger vein recognition. Experimental results show that our equal error rate (EER) is significantly lower compared to that without the proposed method or using a conventional method.
66 citations
Cites methods from "Multimodal biometric authentication..."
...These are ridges, bifurcation, and endings (Kang and Park, 2009)....
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...The input image is determined as genuine or imposter by comparing the MHD to the threshold (Kang and Park, 2009; Lee and Park, 2009)....
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TL;DR: A new finger recognition method based on the score-level fusion of finger veins, fingerprints, and finger geometry features is proposed; its performance is better than the conventional Z-score normalization method and the equal error rate was lower than those of the other methods.
Abstract: Multimodal biometrics provides high recognition accuracy and population coverage by using various biometric features. A single finger contains finger veins, fingerprints, and finger geometry features; by using multimodal biometrics, information on these multiple features can be simultaneously obtained in a short time and their fusion can outperform the use of a single feature. This paper proposes a new finger recognition method based on the score-level fusion of finger veins, fingerprints, and finger geometry features. This research is novel in the following four ways. First, the performances of the finger-vein and fingerprint recognition are improved by using a method based on a local derivative pattern. Second, the accuracy of the finger geometry recognition is greatly increased by combining a Fourier descriptor with principal component analysis. Third, a fuzzy score normalization method is introduced; its performance is better than the conventional Z-score normalization method. Fourth, finger-vein, fingerprint, and finger geometry recognitions are combined by using three support vector machines and a weighted SUM rule. Experimental results showed that the equal error rate of the proposed method was 0.254%, which was lower than those of the other methods.
51 citations
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TL;DR: A brief overview of the field of biometrics is given and some of its advantages, disadvantages, strengths, limitations, and related privacy concerns are summarized.
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"Multimodal biometric authentication..." refers methods in this paper
...be degraded by many factors, such as illumination change, pose variation, noise in the input data, etc.(1) To overcome these problems, we propose a new multimodal recognition method using score level fusion of finger-vein and finger geometry recognition....
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TL;DR: Study of the performance of different normalization techniques and fusion rules in the context of a multimodal biometric system based on the face, fingerprint and hand-geometry traits of a user found that the application of min-max, z-score, and tanh normalization schemes followed by a simple sum of scores fusion method results in better recognition performance compared to other methods.
2,021 citations
"Multimodal biometric authentication..." refers methods in this paper
...are normalized in the range of 0–1 by z-score normalization using the mean and standard deviation of each score distribution.(10)...
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