Sliding Window-Based Region of Interest Extraction for Finger Vein Images

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.

Finger-vein image restoration based on skin optical property

“…In this section, the used finger-vein images for experiment are captured by a homemade transillumination imaging system with the 760nm NIR LED array source, and then obtained from raw images by the ROI localization and segmentation method in [14], [15]. The finger-vein image database contains In order to demonstrate the validity of the proposed method, some obviously degraded finger-vein images are collected as testing samples, as shown in Figure 6(a).…”

Superpixel based finger vein ROI extraction with sensor interoperability

“…We use the middle points of two detected finger boundaries to adjust finger displacement as [16]. First, the middle points of two finger boundaries are computed and synthesized into a straight line, denoted by Formula 2:…”

“…These methods can be roughly grouped into the following four types: (I) predefined window based method [15], (II) sobel operator based method [16], (III) masks based method [7], and (IV) threshold based method [3]. All the existing methods achieve good performance on the database captured by one sensor.…”

Singular value decomposition based minutiae matching method for finger vein recognition

“…Although promising experimental results are reported in [16][17][18][19]; in practice, these ROI-based methods may suffer from some limitations. Firstly, image alignment, one of the most intractable problems in biometric recognitions field [2,15,20,21], is a critical step in most of these methods, resulting from the captured images generally varying in direction and position. Secondly, many features extracted from the non-vein regions are noisy, which inevitably reduces the recognition Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/neucom performance.…”

“…In particular, image capturing visualizes veins in a finger [10,11]. Preprocessing mainly enhances images [12,13], extracts region of interest (ROI) [14,15], etc. Feature extraction detects the characteristics of the vein pattern for representation and matching measures the similarity between two finger vein images for recognition [29,42].…”