A New ROI Extraction Method of Non-contact Finger Vein Images

2013 ◽  
pp. 288-297 ◽  
Author(s):  
Chunting Zuo ◽  
Kejun Wang ◽  
Xinjing Song
Keyword(s):  
Extraction Method ◽  
Finger Vein ◽  
Roi Extraction

scholarly journals A Novel ROI Extraction Method Based on the Characteristics of the Original Finger Vein Image

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4402
Author(s):  
Huimin Lu ◽  
Yifan Wang ◽  
Ruoran Gao ◽  
Chengcheng Zhao ◽  
Yang Li
Keyword(s):  
Extraction Method ◽  
Region Of Interest ◽  
Living Body ◽  
Finger Vein ◽  
Imaging Characteristics ◽  
Vein Imaging ◽  
Roi Extraction ◽  
Vein Recognition ◽  
Public Datasets ◽  
Speed Accuracy

As the second generation of biometric technology, finger vein recognition has become a research hotspot due to its advantages such as high security, and living body recognition. In recent years, the global pandemic has promoted the development of contactless identification. However, the unconstrained finger vein acquisition process will introduce more uneven illumination, finger image deformation, and some other factors that may affect the recognition, so it puts forward higher requirements for the acquisition speed, accuracy and other performance. Considering the universal, obvious, and stable characteristics of the original finger vein imaging, we proposed a new Region Of Interest (ROI) extraction method based on the characteristics of finger vein image, which contains three innovative elements: a horizontal Sobel operator with additional weights; an edge detection method based on finger contour imaging characteristics; a gradient detection operator based on large receptive field. The proposed methods were evaluated and compared with some representative methods by using four different public datasets of finger veins. The experimental results show that, compared with the existing representative methods, our proposed ROI extraction method is 1/10th of the processing time of the threshold-based methods, and it is similar to the time spent for coarse extraction in the mask-based methods. The ROI extraction results show that the proposed method has better robustness for different quality images. Moreover, the results of recognition matching experiments on different datasets indicate that our method achieves the best Equal Error Rate (EER) of 0.67% without the refinement of feature extraction parameters, and all the EERs are significantly lower than those of the representative methods.

scholarly journals Enhanced Region of Interest Extraction method for Finger Vein Images

2021 ◽  
pp. 13-25
Author(s):  
Prof Hindrustum Shaaban
Keyword(s):  
Extraction Method ◽  
Region Of Interest ◽  
Recognition System ◽  
Finger Vein ◽  
Corrected Image ◽  
Effective Manner ◽  
Roi Extraction ◽  
Vein Recognition ◽  
Oblique Images ◽  
Finger Vein Recognition

Extracting Region of Interest (ROI) is an important step for finger vein recognition system. The purpose of this process is to determine the part of the image that we need for extracting features. In this paper we present an ROI extraction method that overcome the problems of finger rotation and displacement. We first locate the finger midline to be used in correcting the oblique images. We then use a sliding window to determine the Proximal inter phalangeal joint and to further identify the ROI height. Finally, from the corrected image of a certain height, the ROI is obtained through the use of finger edges internal tangents as ROI boundaries. The results prove that our method in a more accurate and effective manner in comparison with the method of [1], and thus enhance the performance of the system.

scholarly journals A Novel Finger Vein Recognition Method Based on Aggregation of Radon-Like Features

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1885
Author(s):  
Qiong Yao ◽  
Dan Song ◽  
Xiang Xu ◽  
Kun Zou
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Spatial Information ◽  
Individual Recognition ◽  
Vein Pattern ◽  
Low Contrast ◽  
Feature Extraction Method ◽  
Structure Information ◽  
Finger Vein ◽  
Specific Implementation

Finger vein (FV) biometrics is one of the most promising individual recognition traits, which has the capabilities of uniqueness, anti-forgery, and bio-assay, etc. However, due to the restricts of imaging environments, the acquired FV images are easily degraded to low-contrast, blur, as well as serious noise disturbance. Therefore, how to extract more efficient and robust features from these low-quality FV images, remains to be addressed. In this paper, a novel feature extraction method of FV images is presented, which combines curvature and radon-like features (RLF). First, an enhanced vein pattern image is obtained by calculating the mean curvature of each pixel in the original FV image. Then, a specific implementation of RLF is developed and performed on the previously obtained vein pattern image, which can effectively aggregate the dispersed spatial information around the vein structures, thus highlight vein patterns and suppress spurious non-boundary responses and noises. Finally, a smoother vein structure image is obtained for subsequent matching and verification. Compared with the existing curvature-based recognition methods, the proposed method can not only preserve the inherent vein patterns, but also eliminate most of the pseudo vein information, so as to restore more smoothing and genuine vein structure information. In order to assess the performance of our proposed RLF-based method, we conducted comprehensive experiments on three public FV databases and a self-built FV database (which contains 37,080 samples that derived from 1030 individuals). The experimental results denoted that RLF-based feature extraction method can obtain more complete and continuous vein patterns, as well as better recognition accuracy.

Finger Vein ROI Extraction Based on Robust Edge Detection and Flexible Sliding Window

2017 ◽  
Vol 32 (04) ◽  
pp. 1856002 ◽  
Author(s):  
Mingwen Wang ◽  
Dongming Tang ◽  
Zhangyou Chen
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Finger Joint ◽  
Reference Line ◽  
Detection Methods ◽  
Joint Region ◽  
Sliding Windows ◽  
Finger Vein ◽  
Roi Extraction ◽  
Edge Detection Method

An accurate region of interest extraction (ROI) plays an important role for both finger vein recognition systems and finger vein-based cryptography systems. In order to localize the rectangle ROI accurately, the edges of the finger and a line in the finger joint region should be detected accurately as a reference position. Because most of the existing finger edge detection methods do not work well, a robust finger edge detection method is proposed in this paper. An inner line of the finger is first detected to divide the finger vein image by two parts, after that two edge detection templates and a series of technologies such as interpolation, fit, etc. are used to detect and fix the wrong edges of the finger. Furthermore, considering that the shapes of the brighter finger joint region are irregular, multiple sliding windows including rectangle, disk, diamond and ellipse are generated, respectively to detect the reference line of the finger joint. Finally, a contour similarity distance-based method is introduced to evaluate the performance of various sliding windows. The experimental results show that the proposed edge detection method can 100% successfully detect the edges of the fingers in our finger vein image database. And for various detection windows, the ellipse window is more suitable for the detection of the finger joint reference line. So, the proposed ROI extraction method for finger vein images has a better overall performance compared with the other methods.

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