New Finger Biometric Method Using Near Infrared Imaging

In this paper, we propose a new finger biometric method. Infrared finger images are first captured, and then feature extraction is performed using a modified Gaussian high-pass filter through binarization, local binary pattern (LBP), and local derivative pattern (LDP) methods. Infrared finger images include the multimodal features of finger veins and finger geometries. Instead of extracting each feature using different methods, the modified Gaussian high-pass filter is fully convolved. Therefore, the extracted binary patterns of finger images include the multimodal features of veins and finger geometries. Experimental results show that the proposed method has an error rate of 0.13%

Finger Vein Recognition Based on a Personalized Best Bit Map

Finger vein patterns have recently been recognized as an effective biometric identifier. In this paper, we propose a finger vein recognition method based on a personalized best bit map (PBBM). Our method is rooted in a local binary pattern based method and then inclined to use the best bits only for matching. We first present the concept of PBBM and the generating algorithm. Then we propose the finger vein recognition framework, which consists of preprocessing, feature extraction, and matching. Finally, we design extensive experiments to evaluate the effectiveness of our proposal. Experimental results show that PBBM achieves not only better performance, but also high robustness and reliability. In addition, PBBM can be used as a general framework for binary pattern based recognition

A Comparative Study of Finger Vein Recognition by Using Learning Vector Quantization

¾ This paper presents a comparative study of finger vein recognition using various features with Learning Vector Quantization (LVQ) as a classification method. For the purpose of this study, two main features are employed: Scale Invariant Feature Transform (SIFT) and Local Extensive Binary Pattern (LEBP). The other features that formed LEBP features: Local Multilayer Binary Pattern (LmBP) and Local Directional Binary Pattern (LdBP) are also employed. The type of images are also become the base of comparison. The SIFT features will be extracted from two types of images which are grayscale and binary images. The feature that have been extracted become the input for recognition stage. In recognition stage, LVQ classifier is used. LVQ will classify the images into two class which are the recognizable images and non recognizable images. The accuracy, false positive rate (FPR), and true positive rate (TPR) value are used to evaluate the performance of finger vein recognition. The performance result of finger vein recognition becomes the main study for comparison stage. From the experiments result, it can be found which feature is the best for finger vein reconition using LVQ. The performance of finger vein recognition that use SIFT feature from binary images give a slightly better result than uisng LmBP, LdBP, or LEBP feature. The accuracy value could achieve 97,45%, TPR at 0,9000 and FPR at 0,0129

A comparative study of finger vein recognition by using Learning Vector Quantization

Abstract¾ This paper presents a comparative study of finger vein recognition using various features with Learning Vector Quantization (LVQ) as a classification method. For the purpose of this study, two main features are employed: Scale Invariant Feature Transform (SIFT) and Local Extensive Binary Pattern (LEBP). The other features that formed LEBP features: Local Multilayer Binary Pattern (LmBP) and Local Directional Binary Pattern (LdBP) are also employed. The type of images are also become the base of comparison. The SIFT features will be extracted from two types of images which are grayscale and binary images. The feature that have been extracted become the input for recognition stage. In recognition stage, LVQ classifier is used. LVQ will classify the images into two class which are the recognizable images and non recognizable images. The accuracy, false positive rate (FPR), and true positive rate (TPR) value are used to evaluate the performance of finger vein recognition. The performance result of finger vein recognition becomes the main study for comparison stage. From the experiments result, it can be found which feature is the best for finger vein reconition using LVQ. The performance of finger vein recognition that use SIFT feature from binary images give a slightly better result than uisng LmBP, LdBP, or LEBP feature. The accuracy value could achieve 97,45%, TPR at 0,9000 and FPR at 0,0129. 

Finger Vein Template Protection with Directional Bloom Filter

Biometrics has become a widely accepted solution for secure user authentication. However, the use of biometric traits raises serious concerns about the protection of personal data and privacy. Traditional biometric systems are vulnerable to attacks due to the storage of original biometric data in the system. Because biometric data cannot be changed once it has been compromised, the use of a biometric system is limited by the security of its template. To protect biometric templates, this paper proposes the use of directional bloom filters as a cancellable biometric approach to transform the biometric data into a non-invertible template for user authentication purposes. Recently, Bloom filter has been used for template protection due to its efficiency with small template size, alignment invariance, and irreversibility. Directional Bloom Filter improves on the original bloom filter. It generates hash vectors with directional subblocks rather than only a single-column subblock in the original bloom filter. Besides, we make use of multiple fingers to generate a biometric template, which is termed multi-instance biometrics. It helps to improve the performance of the method by providing more information through the use of multiple fingers. The proposed method is tested on three public datasets and achieves an equal error rate (EER) as low as 5.28% in the stolen or constant key scenario. Analysis shows that the proposed method meets the four properties of biometric template protection. Doi: 10.28991/HIJ-2023-04-02-013 Full Text: PD

Vein Pattern Extraction Using Near Infrared Imaging for Biometric Purposes

Biomedical verification has been broadly examined for many years and pulled in much consideration because of its huge potential security application. Vein is less prone to damage and almost improbable to copy than any other physiological as well as behavioural biometric features such as fingerprint, iris, face and voice recognition. This paper proposes an efficient vein extraction method on low quality vein images taken by a camera absorbing near infrared light (NIR camera). At first, the image is contrast enhanced using contrast limited adaptive histogram equalization (CLAHE); secondly, local threshold method is applied on small blocks of the image followed by several morphological operations such as fill, erosion, dilation, clean and bridge, performed sequentially, for better accuracy. Experimental results obtained for extraction show that the proposed method can reap better results with reduced complexity. After extraction, matching of the test image with the template images stored in the database are matched using minutiae (point-to-point pattern). An orientation detector which filters out missing or unnecessary or unnatural spurious minutiae pairings while simultaneously using path or ridge orientations to increase performance and similarity score calculation. Thus the obtained processed images can be used in biometric purposes which in turn enhances the security of the syste

Multispectral Palmprint Encoding and Recognition

Palmprints are emerging as a new entity in multi-modal biometrics for human identification and verification. Multispectral palmprint images captured in the visible and infrared spectrum not only contain the wrinkles and ridge structure of a palm, but also the underlying pattern of veins; making them a highly discriminating biometric identifier. In this paper, we propose a feature encoding scheme for robust and highly accurate representation and matching of multispectral palmprints. To facilitate compact storage of the feature, we design a binary hash table structure that allows for efficient matching in large databases. Comprehensive experiments for both identification and verification scenarios are performed on two public datasets -- one captured with a contact-based sensor (PolyU dataset), and the other with a contact-free sensor (CASIA dataset). Recognition results in various experimental setups show that the proposed method consistently outperforms existing state-of-the-art methods. Error rates achieved by our method (0.003% on PolyU and 0.2% on CASIA) are the lowest reported in literature on both dataset and clearly indicate the viability of palmprint as a reliable and promising biometric. All source codes are publicly available.Comment: Preliminary version of this manuscript was published in ICCV 2011. Z. Khan A. Mian and Y. Hu, "Contour Code: Robust and Efficient Multispectral Palmprint Encoding for Human Recognition", International Conference on Computer Vision, 2011. MATLAB Code available: https://sites.google.com/site/zohaibnet/Home/code

Multi-feature Fusion Menggunakan Fitur Scale Invariant Feature Transform dan Local Extensive Binary Pattern untuk Pengenalan Pembuluh Darah pada Jari

Pengenalan pembuluh darah jari merupakan salah satu area dalam bidang biometrika. Sehingga tahap-tahap dalam proses pengenalan pembuluh darah jari memiliki kesamaan dengan proses pengenalan menggunakan biometrika lain yaitu meliputi pengumpulan citra, praproses, ekstraksi fitur, dan pencocokan. Tingkat keberhasilan dari tahap pencocokan ditentukan oleh pemilihan fitur pembuluh darah jari yang digunakan. Kondisi citra pembuluh darah yang rentan terhadap perubahan skala, rotasi maupun translasi menyebabkan kebutuhan akan fitur yang tahan terhadap kondisi tersebut menjadi hal yang penting. Fitur Scale Invariant Feature Transform (SIFT) adalah fitur yang telah cukup banyak digunakan untuk kasus pencocokan citra serta mampu tahan terhadap degradasi kondisi citra akibat perubahan skala, rotasi maupun translasi. Akan tetapi, fitur SIFT kurang memberikan hasil optimal jika diekstraksi dari citra dengan variasi tingkat keabuan seperti yang disebabkan oleh perbedaan intensitas pencahayaan. Fitur Local Extensive Binary Pattern (LEBP) merupakan fitur yang tahan terhadap variasi tingkat keabuan dengan informasi karakteristik lokal yang lebih kaya dan diskriminatif. Oleh karena itu digunakan teknik fusi untuk memperoleh informasi dari fitur SIFT dan fitur LEBP sehingga diperoleh fitur yang memiliki ketahanan terhadap degradasi kondisi citra akibat perubahan skala, rotasi, translasi, variasi tingkat keabuan seperti yang disebabkan oleh perbedaan intensitas pencahayaan. Penelitian ini mengusulkan multi-feature fusion menggunakan fitur SIFT dan LEBP untuk pengenalan pembuluh darah pada jari. Fitur hasil fusion diproses dengan metode Learning Vector Quantization (LVQ) untuk menentukan apakah citra pembuluh darah jari yang diuji dapat dikenali atau tidak. Dengan menggunakan multi-feature fusion diharapkan mampu representasi fitur yang dapat meningkatkan akurasi dari proses pengenalan pembuluh darah jari meskipun fitur diambil dari citra yang mengalami degradasi. Berdasarkan hasil uji coba diperoleh bahwa penggunaan multi-feature fusion dengan fitur SIFT dan LEBP memberikan hasil yang relatif lebih baik jika dibandingkan dengan hanya menggunakan fitur tunggal. Hal tersebut dapat dilihat dari peningkatan hasil kinerja sistem pada kondisi optimum dengan nilai akurasi sebesar 97,50%, TPR sebesar 0,9400 dan FPR sebesar 0,0128. ========== Finger vein recognition is one of the areas in the field of biometrics. The steps of finger vein recognition has in common with other biometric recognition process which include image acquisition, preprocessing, feature extraction and matching. The success rate of matching stage is determined by the selection of features. The conditions of finger vein images are susceptible to changes in scale, rotation and translation. The need for features that are resistant to these conditions becomes important. Scale invariant Feature Transform (SIFT) feature is a feature that has been quite widely used for image matching case and be able to withstand degradation due to changes in the condition of the image scale, rotation and translation. However, SIFT feature provide less optimal results when extracted from the image with gray level variations such as those caused by differences in lighting intensity. Local Extensive Binary Pattern (LEBP) feature is a feature that has resistance to gray level variations with richer and discriminatory local characteristics information. Therefore the fusion technique is used to obtain information from SIFT feature and LEBP feature. So that, the feature that has been produced can resist degradation problems such as changes in the condition of the image scale, rotation, translation, and gray level variations which caused by differences in lighting intensity. This study proposes a multi-feature fusion using SIFT and LEBP features for finger vein recognition. This fusion feature will be processed by Learning Vector Quantization (LVQ) method to determine whether the testing image can be x recognized or not. By using a multi-feature fusion, it is expected to get representations of features that can improve the accuracy of the finger vein recognition although the feature is taken from the degraded image. Based on experiment results, finger vein recognition that use multi-feature fusion using integration feature of scale invariant feature transform and local extensive binary pattern provide a better result than only use a single feature. It can be seen from the increase of performance system in optimum condition. The accuracy value can achieve 97.50%, TPR at 0.9400 and FPR at 0.0128