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.

Region of interest extraction for image quality assessment

2020 ◽  
Vol 2020 (9) ◽  
pp. 321-1-321-9
Author(s):  
Runzhe Zhang ◽  
Eric Maggard ◽  
Yousun Bang ◽  
Minki Cho ◽  
Jan Allebach
Keyword(s):  
Digital Image ◽  
Extraction Method ◽  
Image Quality Assessment ◽  
Region Of Interest ◽  
Ground Truth ◽  
Extraction Process ◽  
Print Quality ◽  
Extraction Algorithm ◽  
Color Vector ◽  
Roi Extraction

Print quality (PQ) is most important in the printing industry. To detect and analyze print defects is an effective solution to improve print quality. As the different types of print defects appear in different regions of interest (ROI) in the digital image of a scanned page, extracting the different ROIs helps to detect and analyze the printer defect. This paper proposes a method to extract different ROIs based on the digital image object map [1], which includes three different labels: raster (images or pictures), vector (background and smooth gradient color areas), and symbol (symbols and texts). Our ROI extraction method will extract four kinds of ROIs based on these three labeled objects. So we need to distinguish the background area and smooth gradient color area (color vector) from other vector objects. The process of the ROI extraction method includes four parts; and each part will extract one kind of ROI. For the color vector and background ROI extraction part, we develop two approaches: one is to obtain the maximum area rectangular ROI; and the other approach is to extract the deepest rectangular ROI. With both of these two methods, we use a greedy algorithm to gather additional useful ROIs. In the final result of the ROI extraction process, we only save the left top and right bottom positions for each ROI. In the end, we design a Matlab GUI Tool and label the ROI ground truth manually. We calculate the intersection over union (IoU)) between the ROI extraction result and the ROI manually labeled ground truth to evaluate our ROI extraction algorithm, and check whether it is good enough to crop different ROIs from the image of the scanned page to detect and analyze print defects.

scholarly journals Improving the Performance of Finger-Vein Recognition System Using A New Scheme of Modified Preprocessing Methods

2020 ◽  
Vol 9 (3) ◽  
pp. 397
Author(s):  
Ahmed A. Mustafa ◽  
Ahmed AK. Tahir
Keyword(s):  
Feature Extraction ◽  
Recognition System ◽  
Extraction Methods ◽  
Error Rates ◽  
Least Square ◽  
Base Line ◽  
Finger Vein ◽  
Roi Extraction ◽  
Vein Recognition ◽  
Finger Vein Recognition

This paper aims at improving the performance of finger-vein recognition system using a new scheme of image preprocessing. The new scheme includes three major steps, RGB to Gray conversion, ROI extraction and alignment and ROI enhancement. ROI extraction and alignment includes four major steps. First, finger-vein boundaries are detected using two edge detection masks each of size (4 x 6). Second, the correction for finger rotation is done by calculating the finger base line from the midpoints between the upper and lower boundaries using least square method. Third, ROI is extracted by cropping a rectangle around the center of the finger-vein which is determined using the first and second invariant moments. Forth, the extracted ROI is normalized to a unified size of 192 x 64 in order to compensate for scale changes. ROI enhancement is done by applying the technique of Contrast-Limited Adaptive Histogram Equalization (CLAHE), followed by median and modified Gaussian high pass filters. The application of the given preprocessing scheme to a finger-vein recognition system revealed its efficiency when used with different methods of feature extractors and with different types of finger-vein database. For the University of Twente Finger Vascular Pattern (UTFVP) database, the achieved Identification Recognition Rates (IRR) for identification mode using three feature extraction methods Local Binary Pattern (LBP), Local Directional Pattern (LDP) and Local Line Binary Pattern (LLBP) are (99.79, 99.86 and 99.86) respectively, while the achieved Equal Error Rates (EER) for verification mode for the same feature extraction methods are (0.139, 0.069 and 0.035). For the Shandong University Machine Learning and Applications - Homologous Multi-modal Traits (SDUMLA-HMT) database, the achieved Identification Recognition Rates (IRR) for identification mode using three feature extraction methods LBP, LDP and LLBP are (99.57, 99.73 and 99.65) respectively, while the achieved Equal Error Rates (EER) for verification mode for the same feature extraction methods are (0.419, 0.262 and 0.341). These results outrage those of the previous state-of-art methods.

IRIS AND FINGER VEIN MULTI MODEL RECOGNITION SYSTEM BASED ON SIFT FEATURES

2018 ◽  
Vol 1 (2) ◽  
pp. 34-44
Author(s):  
Faris E Mohammed ◽  
Dr. Eman M ALdaidamony ◽  
Prof. A. M Raid
Keyword(s):  
Iris Recognition ◽  
Recognition Performance ◽  
Recognition System ◽  
Individual Identification ◽  
Work Place ◽  
Identification Process ◽  
Finger Vein ◽  
Noise Point ◽  
Vein Recognition ◽  
A New Technique

Individual identification process is a very significant process that resides a large portion of day by day usages. Identification process is appropriate in work place, private zones, banks …etc. Individuals are rich subject having many characteristics that can be used for recognition purpose such as finger vein, iris, face …etc. Finger vein and iris key-points are considered as one of the most talented biometric authentication techniques for its security and convenience. SIFT is new and talented technique for pattern recognition. However, some shortages exist in many related techniques, such as difficulty of feature loss, feature key extraction, and noise point introduction. In this manuscript a new technique named SIFT-based iris and SIFT-based finger vein identification with normalization and enhancement is proposed for achieving better performance. In evaluation with other SIFT-based iris or SIFT-based finger vein recognition algorithms, the suggested technique can overcome the difficulties of tremendous key-point extraction and exclude the noise points without feature loss. Experimental results demonstrate that the normalization and improvement steps are critical for SIFT-based recognition for iris and finger vein , and the proposed technique can accomplish satisfactory recognition performance. Keywords: SIFT, Iris Recognition, Finger Vein identification and Biometric Systems.   © 2018 JASET, International Scholars and Researchers Association    

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