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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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ROC curves of the 1st experiment on the three databases (DBs).
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sensors-15-16866-f011: ROC curves of the 1st experiment on the three databases (DBs).

Mentions: Table 3 shows the comparative results of the four experiments defined in Table 2 and Figure 10 for the three databases. In the 1st experiment, in which finger-vein images were classified by fingers, hands, and people, the lowest EER (0.474%) was obtained for the good-quality database. This is due to the fact that this database was captured by the proposed capturing device, which uses a guiding bar to reduce the misalignment among input finger-vein images. In the case of the open database, the authors did not apply any guiding bar for alignment in the image-capturing device [29]. As a result, the EER obtained from this database was the highest (8.096%) because of the misalignment of captured fingers. The results of the first experiment also indicate that the matching accuracies from images in the good-quality database were the highest, followed by those in the mid-quality database, whereas the worst matching accuracies were obtained for the open database, in terms of EERs (0.474%, 2.393%, and 8.096%, respectively). These results correspond to the level of misalignment in each finger-vein database. The resulting plots of the ROC curves and matching distance distributions obtained from the experiments classified by fingers, hands, and people for the three databases are shown in Figure 11 and Figure 12.


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)

ROC curves of the 1st experiment on the three databases (DBs).
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-16866-f011: ROC curves of the 1st experiment on the three databases (DBs).
Mentions: Table 3 shows the comparative results of the four experiments defined in Table 2 and Figure 10 for the three databases. In the 1st experiment, in which finger-vein images were classified by fingers, hands, and people, the lowest EER (0.474%) was obtained for the good-quality database. This is due to the fact that this database was captured by the proposed capturing device, which uses a guiding bar to reduce the misalignment among input finger-vein images. In the case of the open database, the authors did not apply any guiding bar for alignment in the image-capturing device [29]. As a result, the EER obtained from this database was the highest (8.096%) because of the misalignment of captured fingers. The results of the first experiment also indicate that the matching accuracies from images in the good-quality database were the highest, followed by those in the mid-quality database, whereas the worst matching accuracies were obtained for the open database, in terms of EERs (0.474%, 2.393%, and 8.096%, respectively). These results correspond to the level of misalignment in each finger-vein database. The resulting plots of the ROC curves and matching distance distributions obtained from the experiments classified by fingers, hands, and people for the three databases are shown in Figure 11 and Figure 12.

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