Finger Vein Identification using Fuzzy-based k-Nearest Centroid Neighbor Classifier (original) (raw)
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LOCAL LINE BINARY PATTERN AND FUZZY K-NN FOR PALM VEIN RECOGNITION
Journal of Theoretical and Applied Information Technology, 2017
Recently, palm vein recognition has been studied to overcome the problem in terms of convenience and performance of conventional systems in biometrics technologies such as fingerprint, palm print, face, and iris recognitions. However, palm vein images that are used in palm vein recognition systems are not always clear but sometimes can show irregular shadings and highly saturated regions that can slow the processing time. To overcome this problem, we propose palm vein recognition system using Local Line Binary Pattern (LLBP) method that was reliable against irregular shadings and highly saturated regions. LLBP is a texture descriptor based on the gray level comparison of a neighborhood of pixels. Proposed method have been conducted in three major steps: preprocessing that includes Region of Interest (ROI) detection, image resizing, noise removal and image enhancement, feature extraction using LLBP method, and matching using Fuzzy k-NN classifier. We use CASIA Multi-Spectral Image Database as dataset to examine proposed method. Experimental results show that the proposed method using LLBP has a good performance with 93.2% recognition accuracy.
Enhanced Finger Vein Based Recognition System
International Journal of Intelligent Computing and Information Sciences, 2016
Robust Recognition systems become more complicated over time. These systems are derived from features which can be extracted from different body members using extractor methods. Finger vein is suitable member that could be used to violate the weakness of finger print. Conventional extractor methods like matched filter and morphological methods can extricate patterns if the widths of veins are steady whereas repeated line tracking method extract vein patterns from a hazy picture. These strategies can't remove veins that are smaller extensive than the accepted widths which corrupts the precision of the individual recognizable proof or can't adequately extricate flimsy veins on the grounds. In turn, we have proposed a system that tackles these issues by checking the shape of the picture profiles and stressing just the centerlines of veins. Our system for distinguishing the most extreme bend positions is hearty against transient vacillations in vein width and splendor. This paper introduces a finger vein recognition system based on using histogram of gradient and multi class support vector machine and finger vein recognition is powered by using Gabor filter with classifier powered by multi class support vector machine. The proposed have great enhancement impact over relative to accuracy, sensitivity, F-measure and precision during evaluation.
Finger Vein Recognition Based on Anatomical Features of Vein Patterns
IEEE Access
Finger vein recognition is a promising biometric authentication technique that depends on the unique features of vein patterns in the finger for recognition. The existing finger vein recognition methods are based on minutiae features or binary features such as LBP, LLBP, PBBM etc. or from the entire vein pattern. However, the minutiae-based features cannot accurately represent the structural or anatomical aspects of the vein pattern. These issues with the minutia feature led to increased false matches. Recognition based on binary features have limitations such as increased false matches, sensitivity to the translation and rotation, security and privacy issues etc. A feature representation based on the anatomy of vein patterns can be an alternative solution to improve the recognition performance. In the IJCB 2020 conference, we showed that every finger vein image contains one or more of a kind of 4 special vein patterns which we refereed as Fork, Eye, Bridge, and Arch (FEBA). In this paper, we further enlarge this set to 6 vein patterns (F 1 F 2 EB 1 B 2 A) by identifying two variations in the Fork and Bridge vein patterns. Based on 6 anatomical features of the possible 6 vein patterns in a vein image, we define a 6 × 6 feature matrix representation for finger vein images. Since this feature representation is based on the anatomical properties of the local vein patterns, it provides template security. Further we show that, the proposed feature representation is invariant to scaling, translation, and rotation changes. The experimental results using two open datasets and an in-house dataset show that the proposed method has a better recognition performance when compared to the existing approaches with an EER around 0.02% and an average recognition accuracy of 98%. INDEX TERMS FEBA Classification, F 2 EB 2 A, Feature Representation, Finger Vein Biometrics, Matching, Vein Anatomy.
International Journal of Computer Sciences and Engineering, 2018
Biometric based system securities are superior because they provide a non-transferable means of identifying people not just cards, password or PINs. However many biometric traits are not secure against forgery and spoofing which breaks the biometric security systems. To overcome these security problems of previous biometric systems, people are looking towards vein biometrics which uses unique vascular pattern from human body. Finger vein biometrics has become most promising biometric recognition system due to its accuracy, security and convenience. Recently many researchers are working for developing novel approaches for finger vein pattern based biometric recognition system. This paper presents an approach for personal identification using local and global feature of finger vein pattern images. The local directional features of vein pattern are extracted using Local Directional features method and global texture features extracted using Discrete Wavelet Transform (DWT) and Rotated Wavelet Filters (RWF) jointly. The feature vector is created by combining the local directional code based features with DWT and RWF based features together at feature level. Then, Canberra distance metric is used for the similarity measurement and classification. Experimental results have shown that the performance of proposed method outperforms other methods in terms of recognition accuracy and error rate.
Springer , 2023
Finger vein patterns contain highly discriminative characteristics, which are difficult to be forged due to residing underneath the skin. Several pieces of research have been carried out in this field but there is still an unresolved issue when data capturing and processing is of low quality. Low-quality data have caused errors in the feature extraction process and reduced identification performance rate in finger vein identification. The objective of this paper is to address this issue by presenting two methods, a new image enhancement, and a feature extraction method. The image enhancement, Composite Median-Wiener (CMW) filter, improves image quality and preserves the edges. Moreover, the feature extraction method, Hierarchical Centroid Feature Method (HCM), is fused with the statistical pixel-based distribution feature method at the feature-level fusion to improve the performance of finger vein identification. These methods were evaluated on public SDUMLA-HMT and FV-USM finger vein databases. Each database was divided into training and testing sets. The average result of the experiments conducted was taken to ensure the accuracy of the measurements. The k-Nearest Neighbor classifier with city block distance to match the features was implemented. Both these methods produced accuracy as high as 97.64% for identification rate and 1.11% of equal error rate (EER) for measures verification rate. These showed that the accuracy of the proposed finger vein identification method is higher than the existing methods. The results have proven that the CMW filter and HCM have significantly improved the accuracy of finger vein identification.
Biometric recognition using finger and palm vein images
Soft Computing, 2018
In recent times, biometrics is the best alternative for the token-based and knowledge-based security systems. Out of the existing biometric modalities, the vascular biometric modalities are preferred for authenticating the person, because of its uniqueness among all individuals. This paper proposes a multimodal biometric system using vascular patterns of the hand such as finger vein and palm vein images. Initially, the input palm vein and finger vein images are pre-processed so as to make them suitable for further processing. Subsequently, the features from palm and finger vein images are extracted using a modified two-dimensional Gabor filter and a gradient-based techniques. These extracted features are matched using the Euclidean distance metric, and they are fused at the score level using fuzzy logic technique. The proposed technique is tested on the standard databases of finger vein and palm vein images. This method provides lower false acceptance rate, false rejection rate and high accuracy of 99.5% when compared with the existing techniques, indicating the effectiveness of the proposed system.
An Improved Palm Vein Based Recognition System
Though biometrics techniques has been recording high level of security when compared with other forms of authentication, it still come with challenges of speed and accuracy of the technique been used. In this paper an improved palm vein based recognition system was developed and implemented. The development procedure was divided into four stages which are Image enhancement, Image segmentation, Image thinning and Pattern Matching. The Image was enhanced using Histogram Equalization, after which it was passed for Segmentation by K-Means algorithm. The binarized image from K-Means was then thinned using the Zhang Suen's algorithm. The Pattern Matching section of the project was done using the Euclidean Distance. Inter-distances of the intersections from the thinned image were stored in a database for subsequent matching. Results from the various test carried out showed that the system has high speed and accuracy.
Academic Journal of Nawroz University (AJNU), 2020
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.
High Accuracy Recognition Bio Metrics Based on Finger Vein Screening Sensor
Iraqi Journal of Information & Communications Technology, 2020
One of the safest biometrics of today is finger vein- but this technic arises with some specific challenges, the most common one being that the vein pattern is hard to extract because finger vein images are always low in quality, significantly hampered the feature extraction and classification stages. Professional algorithms want to be considered with the conventional hardware for capturing finger-vein images is by using red Surface Mounted Diode (SMD) leds for this aim. For capturing images, Canon 750D camera with micro lens is used. For high quality images the integrated micro lens is used, and with some adjustments it can also obtain finger print. Features extraction was used by a combination of Hierarchical Centroid and Histogram of Gradients. Results were evaluated with K Nearest Neighbor and Deep Neural Networks using 6 fold stratified cross validation. Results displayed improvement as compared to three latest benchmarks in this field that used 6-fold validation and SDUM...