Robust Finger-vein ROI Localization Based on the 3<i>σ</i> Criterion Dynamic Threshold Strategy

• Main Authors: Qiong Yao, Dan Song, Xiang Xu
• Format: Article
• Language: English
• Published: MDPI AG 2020-07-01
• Series: Sensors
• Subjects: finger-vein; ROI localization; Kirsch detector; 3σ criterion; dynamic threshold
• Online Access: https://www.mdpi.com/1424-8220/20/14/3997

Region of interest (ROI) localization is one of the key preprocessing technologies for a finger-vein identification system, so an effective ROI definition can improve the matching accuracy. However, due to the impact of uneven illumination, equipment noise, as well as the distortion of finger position, etc., these make accurate ROI localization a very difficult task. To address these issues, in this paper, we propose a robust finger-vein ROI localization method, which is based on the 3<inline-formula> <math display="inline"> <semantics> <mi>σ</mi> </semantics> </math> </inline-formula> criterion dynamic threshold strategy. The proposed method includes three main steps: First, the Kirsch edge detector is introduced to detect the horizontal-like edges in the acquired finger-vein image. Then, the obtained edge gradient image is divided into four parts: upper-left, upper-right, lower-left, and lower-right. For each part of the image, the three-level dynamic threshold, which is based on the 3<inline-formula> <math display="inline"> <semantics> <mi>σ</mi> </semantics> </math> </inline-formula> criterion of the normal distribution, is imposed to obtain more distinct and complete edge information. Finally, through labeling the longest connected component at the same horizontal line, two reliable finger boundaries, which represent the upper and lower boundaries, respectively, are defined, and the ROI is localized in the region between these two boundaries. Extensive experiments are carried out on four different finger-vein image datasets, including three publicly available datasets and one of our newly developed finger-vein datasets with 37,080 finger-vein samples and 1030 individuals. The experimental results indicate that our proposed method has very competitive ROI localization performance, as well as satisfactory matching results on different datasets.