Personal Identification using Local and Global Feature of Finger Vein Patterns using SVM Based Classification (original) (raw)
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FINGER VEIN RECOGNITION BY COMBINING GLOBAL AND LOCAL FEATURES BASED ON SVM
Recently, biometrics such as fingerprints, faces and irises recognition have been widely used in many applications including door access control, personal authentication for computers, internet banking, automatic teller machines and border-crossing controls. Finger vein recognition uses the unique patterns of finger veins to identify individuals at a high level of accuracy. This paper proposes new algorithms for finger vein recognition. This research presents the following three advantages and contributions compared to previous works. First, we extracted local information of the finger veins based on a LBP (Local Binary Pattern) without segmenting accurate finger vein regions. Second, the global information of the finger veins based on Wavelet transform was extracted. Third, two score values by the LBP and Wavelet transform were combined by the SVM (Support Vector Machine). As experimental results, the EER (Equal Error Rate) was 0.011% and the total processing time was 98.2 ms.
Finger Vein Recognition using Rotated Wavelet Filters
International Journal of Computer Applications, 2016
Finger vein biometric have been recognized as the most effective and promising recognition method due to its accuracy and security. This paper discusses a method for finger vein image features extraction using 2-D Rotated Wavelet Filters (RWF) and Discrete Wavelet Transform (DWT) jointly. A set of 2-D RWF filters improves characterization of diagonally oriented texture features from a finger vein image. The 2-D RWF and DWT jointly used for decomposition of a finger vein image ROI up to third level. The standard deviation and energy of each subband from every decomposition level are used for the creation of features vector. Then Canberra distance classifier is used for the classification of finger vein images. The performance of this method has evaluated on the standard finger vein image database of Shandong University (SDUMLA), China. Experimental results have shown that the method with RWF and DWT jointly gives better results as compare to the traditional DWT based methods.
A BIOMETRIC RECOGNITION SYSTEM FOR HUMAN IDENTIFICATION USING FINGER VEIN PATTERNS
Keeping the private information more secure and safer, it has become a challenging part. The biometrics, which uses human physiological or behavioural features for personal identification is a promising alternative for protecting the private information. There are many types of biometric patterns, but no biometric is perfectly reliable or secure. The vein pattern is hidden inside the body and hence human eyes cannot view the vein pattern. By this, the problem of forgery can be reduced. A new quality estimation algorithm is proposed to estimate the quality of vein and the vein image is enhanced using multi scale matched filtering. For vein extraction, information provided by the enhanced image and the vein quality is consolidated using SVM classifier. The proposed vein extraction can handle the issues of hair, skin texture and variable veins widths so as to extract the genuine veins accurately. Experimental results reveal that the proposed system has achieved an accuracy of 98.59 % and it performs well than other existing systems and be a helpful tool for the security purpose.
2018
Finger vein is a promising biometric pattern for personal identification and authentication in terms of its security and convenience when compared to other biometrics. In this project, we compare different feature extraction techniques based on their performance. First the input image is pre-processed and then feature extracted. The methodologies like Gray Level Cooccurrence Matrix (GLCM), Haar wavelet, Curvelet, Local Binary Pattern (LBP) are used for feature extraction of finger vein images. These methods are compared using performance evaluation metrics like FAR (false acceptance rate), FRR (false rejection rate), Recognition rate, accuracy and time with the help of multi SVM (Support Vector Machine) I.INTRODUCTION Biometric technology refers to a pattern recognition system which depends on physical or behavioral features for the person identification. Many biometric systems exist today by using fingerprint, face, iris, voice, palm print, signature, etc. Finger print is a popular...
Texture Based Vein Biometrics for Human Identification: A Comparative Study
2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), 2018
Hand vein biometric is an important modality for human authentication and liveness detection in many applications. Reliable feature extraction is vital to any biometric system. Over the past years, two major categories of vein features, namely vein structures and vein image textures, were proposed for hand dorsal vein based biometric identification. Of them, texture features seem important as it can combine skin microtextures along with vein properties. In this study, we have performed a comparative study to identify potential texture features and feature-classifier combination that produce efficient vein biometric systems. Seven texture features (HOG, GABOR, GLCM, SSF, DWT, WPT, and LBP) and three multiclass classifiers (LDA, ESVM, and KNN) were explored towards the supervised identification of human from vein images. An experiment with 400 infrared (IR) hand images from 40 adults indicates the superior performance of the histogram of oriented gradients (HOG) and simple local statistical feature (SSF) with LDA and ESVM classifiers in terms of average accuracy (> 90%), average Fscore (> 58%) and average specificity (>93%). The decision-level fusion of the LDA and ESVM classifier with single texture features showed improved performances (by 2.2 to 13.2% of average Fscore) over individual classifier for human identification with IR hand vein images.
Hand vein authentication based wavelet feature extraction
2015
Biometrics is a growing scientific field. It aims to identify, through technological systems, an individual, using biological characteristics (eg details of hand, iris, ear, hand lines, fingerprints, gait, posture,). The Using of this technique is now generalized worldwide and takes an important place in everyday life. In the coming years, biometrics will probably be one of the techniques used, first to identify or authenticate individuals and also to control and manage access to material resources, particularly in the following sectors: banking, airports, bus and railway stations, hospitals, private and public institutions, homes, smart cars, museums, ...).The aim of our study is to build a dorsal hand vein database and test our approach on it. Just like any recognition system this last is composed of four steps: the acquisition, enhancement, feature extraction and classification. This paper presents the building protocol of a new database SAB11 BIOM14. Applying some enhancement on...
Lecture Notes in Electrical Engineering, 2020
With the large number of people travelling internationally, there is an increasing demand to be able to deal with security clearance rapidly and with a minimum of inconvenience. Using finger vein biometric traits fulfils these requirements. In previously-reported work, the data obtained from finger veins underwent dimensionality reduction using principal components analysis (PCA) followed by linear discriminant analysis (LDA) and this was shown to improve the identification rate compared to the more commonly applied Discrete Wavelet Transform (DWT). Although PCA was found to be effective at reducing the noise residing in the discarded dimension, this work demonstrates that the corresponding eigenvalue may in fact also contain useful local information that is important in identification and so should be retained. To overcome this problem, this paper proposes the use of feature extraction using DWT and local binary patterns (LBPs) to generate the feature vectors, before they undergo dimensionality reduction using PCA. Support Vector Machines (SVMs) are used for classification. The performance of the proposed method was compared with previous work, with the identification rate of the proposed method offering the best accuracy of 95.8%.
Palm Vein Authentication using Image Classification Technique
Journal 4 Research, 2017
This paper presents an optimized palm vein authentication algorithm which will match the similar type of vascular pattern from the database given i.e. CASIA-Palmprint V1 dataset.. The palm vein authentication technology offers a high level of accuracy. Palm vein authentication uses the vascular patterns of an individual's palm as personal identification data. If we compare with a finger or the back of a hand, a palm has a broader and more complicated vascular pattern and thus contains a wealth of differentiating features for personal identification. The importance of biometrics in the current field of Security has been depicted in this work. Also, we have also outlined opinions about the utility of Biometric authentication systems. We have processed the raw image from the dataset before implementing authentication algorithm. After getting the suitable image after pre-processing, we have used local binary pattern (LBP) for feature extraction purpose & then using a machine learning algorithm, with support vector machine (SVM), we tried to match the vascular vein pattern for authentication. Result of the matching algorithm is not only optimized as per the proposed approach but also quite efficient.
2017
In this paper, the finger images obtained from the database are separated into finger texture and vein images. These two images are processed separately as per the concept presented in paper. The steps involved in matching are divided into image preprocessing, image enhancement, feature extraction and feature matching. For feature extraction we have used Gabor filter and for matching we have implemented score level combination as holistic and nonlinear fusion. We compare holistic and nonlinear algorithm and proved that nonlinear is better than holistic. Finger vein and finger texture matching system has better than the existing security systems. The vein pattern is not detectable to human vision without any special device and it will not produce any trace in any object.
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.