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Nonintrusive Finger-Vein Recognition System Using NIR Image Sensor and Accuracy Analyses According to Various Factors.

Pham TD, Park YH, Nguyen DT, Kwon SY, Park KR - Sensors (Basel) (2015)

Bottom Line: Therefore, fingerprint and iris recognitions are preferred alternatives.Therefore, we propose a nonintrusive finger-vein recognition system using a near infrared (NIR) image sensor and analyze its accuracies considering various factors.The experimental results obtained with three databases showed that our system can be operated in real applications with high accuracy; and the dissimilarity of the finger-veins of different people is larger than that of the finger types and hands.

View Article: PubMed Central - PubMed

Affiliation: Division of Electronics and Electrical Engineering, Dongguk University, 26 Pil-dong 3-ga, Jung-gu, Seoul 100-715, Korea. phamdanhtuyen@gmail.com.

ABSTRACT
Biometrics is a technology that enables an individual person to be identified based on human physiological and behavioral characteristics. Among biometrics technologies, face recognition has been widely used because of its advantages in terms of convenience and non-contact operation. However, its performance is affected by factors such as variation in the illumination, facial expression, and head pose. Therefore, fingerprint and iris recognitions are preferred alternatives. However, the performance of the former can be adversely affected by the skin condition, including scarring and dryness. In addition, the latter has the disadvantages of high cost, large system size, and inconvenience to the user, who has to align their eyes with the iris camera. In an attempt to overcome these problems, finger-vein recognition has been vigorously researched, but an analysis of its accuracies according to various factors has not received much attention. Therefore, we propose a nonintrusive finger-vein recognition system using a near infrared (NIR) image sensor and analyze its accuracies considering various factors. The experimental results obtained with three databases showed that our system can be operated in real applications with high accuracy; and the dissimilarity of the finger-veins of different people is larger than that of the finger types and hands.

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Related in: MedlinePlus

Results of the 2nd, 3rd, and 4th experiments on the mid-quality database: (a) ROC curves of the results of the three experiments; matching distribution of (b) the experiment classified by people (2nd experiment); (d) the experiment classified by hands (3rd experiment); and (f) the experiment classified by fingers (4th experiment), each shown with its corresponding false rejection error case: (c) images of the right ring and left middle fingers of the same person; (e) images of the ring and index fingers on right hands of two different people; and (g) images of the ring fingers on right hands of two different people.
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sensors-15-16866-f014: Results of the 2nd, 3rd, and 4th experiments on the mid-quality database: (a) ROC curves of the results of the three experiments; matching distribution of (b) the experiment classified by people (2nd experiment); (d) the experiment classified by hands (3rd experiment); and (f) the experiment classified by fingers (4th experiment), each shown with its corresponding false rejection error case: (c) images of the right ring and left middle fingers of the same person; (e) images of the ring and index fingers on right hands of two different people; and (g) images of the ring fingers on right hands of two different people.

Mentions: The plots of the ROC curves and matching distributions of authentic and imposter tests obtained from the three experiments (the 2nd, 3rd, and 4th experiments) as well as the error cases for the good-quality, mid-quality, and open databases are shown in Figure 13, Figure 14 and Figure 15, respectively. In the 2nd experiment (classified by people), the cases for which a false rejection was obtained were for different fingers from the same person. The false rejection cases of the 3rd experiment (classified by hands) were the matching pair of vein images of fingers from the same hand side, but belonging to different people or captured from different fingers. Similarly, the false rejections of the 4th experiment (classified by fingers) are cases in which images were captured from the same finger types (i.e., index, middle, or ring fingers) but belonged to different people or hand sides.


Nonintrusive Finger-Vein Recognition System Using NIR Image Sensor and Accuracy Analyses According to Various Factors.

Pham TD, Park YH, Nguyen DT, Kwon SY, Park KR - Sensors (Basel) (2015)

Results of the 2nd, 3rd, and 4th experiments on the mid-quality database: (a) ROC curves of the results of the three experiments; matching distribution of (b) the experiment classified by people (2nd experiment); (d) the experiment classified by hands (3rd experiment); and (f) the experiment classified by fingers (4th experiment), each shown with its corresponding false rejection error case: (c) images of the right ring and left middle fingers of the same person; (e) images of the ring and index fingers on right hands of two different people; and (g) images of the ring fingers on right hands of two different people.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541912&req=5

sensors-15-16866-f014: Results of the 2nd, 3rd, and 4th experiments on the mid-quality database: (a) ROC curves of the results of the three experiments; matching distribution of (b) the experiment classified by people (2nd experiment); (d) the experiment classified by hands (3rd experiment); and (f) the experiment classified by fingers (4th experiment), each shown with its corresponding false rejection error case: (c) images of the right ring and left middle fingers of the same person; (e) images of the ring and index fingers on right hands of two different people; and (g) images of the ring fingers on right hands of two different people.
Mentions: The plots of the ROC curves and matching distributions of authentic and imposter tests obtained from the three experiments (the 2nd, 3rd, and 4th experiments) as well as the error cases for the good-quality, mid-quality, and open databases are shown in Figure 13, Figure 14 and Figure 15, respectively. In the 2nd experiment (classified by people), the cases for which a false rejection was obtained were for different fingers from the same person. The false rejection cases of the 3rd experiment (classified by hands) were the matching pair of vein images of fingers from the same hand side, but belonging to different people or captured from different fingers. Similarly, the false rejections of the 4th experiment (classified by fingers) are cases in which images were captured from the same finger types (i.e., index, middle, or ring fingers) but belonged to different people or hand sides.

Bottom Line: Therefore, fingerprint and iris recognitions are preferred alternatives.Therefore, we propose a nonintrusive finger-vein recognition system using a near infrared (NIR) image sensor and analyze its accuracies considering various factors.The experimental results obtained with three databases showed that our system can be operated in real applications with high accuracy; and the dissimilarity of the finger-veins of different people is larger than that of the finger types and hands.

View Article: PubMed Central - PubMed

Affiliation: Division of Electronics and Electrical Engineering, Dongguk University, 26 Pil-dong 3-ga, Jung-gu, Seoul 100-715, Korea. phamdanhtuyen@gmail.com.

ABSTRACT
Biometrics is a technology that enables an individual person to be identified based on human physiological and behavioral characteristics. Among biometrics technologies, face recognition has been widely used because of its advantages in terms of convenience and non-contact operation. However, its performance is affected by factors such as variation in the illumination, facial expression, and head pose. Therefore, fingerprint and iris recognitions are preferred alternatives. However, the performance of the former can be adversely affected by the skin condition, including scarring and dryness. In addition, the latter has the disadvantages of high cost, large system size, and inconvenience to the user, who has to align their eyes with the iris camera. In an attempt to overcome these problems, finger-vein recognition has been vigorously researched, but an analysis of its accuracies according to various factors has not received much attention. Therefore, we propose a nonintrusive finger-vein recognition system using a near infrared (NIR) image sensor and analyze its accuracies considering various factors. The experimental results obtained with three databases showed that our system can be operated in real applications with high accuracy; and the dissimilarity of the finger-veins of different people is larger than that of the finger types and hands.

Show MeSH
Related in: MedlinePlus