Abstract
Region of Interest (ROI) extraction is a crucial step in an automatic finger vein recognition system. The aim of ROI extraction is to decide which part of the image is suitable for finger vein feature extraction. This paper proposes a finger vein ROI extraction method which is robust to finger displacement and rotation. First, we determine the middle line of the finger, which will be used to correct the image skew. Then, a sliding window is used to detect the phalangeal joints and further to ascertain the height of ROI. Last, for the corrective image with certain height, we will obtain the ROI by using the internal tangents of finger edges as the left and right boundary. The experimental results show that the proposed method can extract ROI more accurately and effectively compared with other methods, and thus improve the performance of finger vein identification system. Besides, to acquire the high quality finger vein image during the capture process, we propose eight criteria for finger vein capture from different aspects and these criteria should be helpful to some extent for finger vein capture.
Highlights
Biometric technology uses inherent behavior or physiological characteristics for personal identification with high security and convenience [1].Compared with other biometrics, finger veins can be seen as a new biometric technology that is attracting more attention from the biometrics research community
In order to ascertain the performance of our Region of Interest (ROI) extraction method, we performed rigorous experiments on the finger vein image database from Hong Kong Polytechnic University [8]
We present a robust ROI extraction method
Summary
Biometric technology uses inherent behavior or physiological characteristics (e.g., fingerprints, faces, irises, finger vein, voices, gait, etc.) for personal identification with high security and convenience [1].Compared with other biometrics, finger veins can be seen as a new biometric technology that is attracting more attention from the biometrics research community. Biometric technology uses inherent behavior or physiological characteristics (e.g., fingerprints, faces, irises, finger vein, voices, gait, etc.) for personal identification with high security and convenience [1]. Finger vein identification promises uniqueness and permanence like other biometric authentication techniques, and has the following advantages over other biometric authentication techniques [1,2]: (1) contactless: non-invasive and non-contact data capture ensures cleanliness for the users, and can affectively avoid forging characteristics on the finger surface of users too; (2) live body identification: identification of finger vein patterns can only be taken on a live body; (3) high security: finger vein patterns are internal features that are difficult to forge. No unified defined standard can be used to regulate image capturing, so it is inevitable that there are a certain number of low quality finger vein images in the captured images. Low quality images will lead to unpromising identification results after time-consuming preprocessing and complicated feature extraction
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