Finger Knuckle Analysis: Gabor Vs DTCWT

Knuckle biometrics is one of the current trends in biometric human identification which offers a reliable solution for verification. This paper analysis FKP recognition based on the behaviour of two different filtering and classification methods. Firstly, Gabor Filter Banks techniques are applied for finger knuckle print recognition and then the same database is analysed against Dual Tree Complex Wavelet Transform technique. The experiment is evaluated to identify finger knuckle images using PolyU FKP database of 7920 images. Finally, these two different systems are compared for false acceptance rate FAR, true acceptance, false rejection rate FRR and true rejection. Extensive experiments are performed to evaluate both the techniques, and experimental results show the pros and cons of using both the techniques for specific applications. DOI: 10.17762/ijritcc2321-8169.150518

A method for delineation of bone surfaces in photoacoustic computed tomography of the finger

Photoacoustic imaging of interphalangeal peripheral joints is of interest in the context of using the synovial membrane as a surrogate marker of rheumatoid arthritis. Previous work has shown that ultrasound produced by absorption of light at the epidermis reflects on the bone surfaces within the finger. When the reflected signals are backprojected in the region of interest, artifacts are produced, confounding interpretation of the images. In this work, we present an approach where the photoacoustic signals known to originate from the epidermis, are treated as virtual ultrasound transmitters, and a separate reconstruction is performed as in ultrasound reflection imaging. This allows us to identify the bone surfaces. Further, the identification of the joint space is important as this provides a landmark to localize a region-of-interest in seeking the inflamed synovial membrane. The ability to delineate bone surfaces allows us not only to identify the artifacts, but also to identify the interphalangeal joint space without recourse to new US hardware or a new measurement. We test the approach on phantoms and on a healthy human finger

Register Transfer Level Implementation Of Pooling - Based Feature Extraction For Finger Vein Identification

Recently, finger vein biometric identification methods have had more attention among the researchers due to its various advantages such as: uniqueness to individuals, immunity to ages and invisibility to human eye (hard to duplicate). Many improvements methods were utilized to increase the speed and accuracy of the identification. Feature extraction techniques based on global feature extraction such as Principle Component Analysis (PCA) were implemented. However, the results did not show robustness to occlusions and misalignments on the finger vein images. Therefore, local feature extraction techniques were used to overcome these issues. A pooling based feature extraction technique for finger vein identification was implemented in this research. The proposed algorithm extracted the local feature information of the finger vein pattern (patches), and used these patches to improve the robustness of the identification. The algorithm was mainly inspired by spatial pyramid pooling in generic image classification combined with PCA. With patch size = 4, four pyramid levels = [1x1, 2x2, 3x3, 4x4] and ~38 % dimension reduction on the extracted features vector (10 PCA coefficient), the accuracy of the identification was 88.69 % which was higher than PCA by 10.10%. The proposed algorithm was implemented on hardware using Verilog-HDL, and targeting Field Programmable Gate Array (FPGA) applications. The result showed an outstanding speed improvement compared to software implementation. The time consumed by the hardware for extracting the features of one image was 310X time faster than the consumed time for software implementation. With those improvements in accuracy and the speed, the proposed algorithm contributes to the advancement of finger vein biometric system

Identifikasi Manusia Dengan Analisis Ciri Fisis Citra Ruas Jari Berbasis Filter 2D Gabor Wavelet Dan JST Learning Vector Quantization (LVQ)

ABSTRAKSI: Kata Kunci : ABSTRACT: Identification techniques have been widely applied for recognition of human identity, many of which are in the form of a password security system or identity card. Where the introduction of human identity through fingerprint, iris, face and DNA, has been done and researched accuration value. While the introduction of human identity through newly developed finger knuckles. In this final has been researched, designed and analyzed a system that can identify people using finger knuckle. Imagery used is finger left hand, a finger, sweet and center. Using digital image processing and extraction feature using 2D Gabor wavelet filter and ANN classification process using Learning Vector Quantization (LVQ). the selection process vector feature value used additional techniques such as Feature Wrapper Subset Selection. At the end of the task previously used methods PCA feature extraction and classification using the K-NN values obtained perfect accuracy of 100%.However, in this thesis by using 2D Gabor wavelet filter and classification method Learning Vector Quantization (LVQ) results obtained from the value of accuracy in identifying human identity value generated the highest accuracy of 77.5% on the training images and 41.67% on the test images to 24 vector characteristics, while using 15 vector characteristics of the resulting value of the highest accuracy of 38.33% on test images. It can be concluded that by using the method of feature extraction of 2D Gabor wavelet filter is not very reliable and appropriate for the type of media biometric finger knucklesKeyword: Identify, finger knuckles, 2D Gabor wavelet filter, LVQ, digital image processing, wrappe

User Identification System Based On Finger-Vein Patterns Using Convolutional Neural Network

Finger-vein biometric identification has gained attention recently due to its several advantages over fingerprint biometric traits. Finger-vein recognition is a method of biometric authentication that applies pattern recognition techniques based on the image of human finger-vein patterns. This paper is focused on developing a MATLAB-based finger-vein recognition system using Convolutional Neural Network (CNN) with Graphical User Interface (GUI) as the user input. Two layers of CNN out of the proposed four-layer CNN have been used to retrain the network for new incoming subjects. The pre-processing steps for finger-vein images and CNN design have been conducted pm different platforms. Therefore, this paper discusses the method of linking both parts from different platforms using MEX-files in MATLAB. Evaluation is carried out using images of 50 subjects that are developed in-house. An accuracy of an average of 96% is obtained to recognize 1 to 10 new subjects within less than 10 seconds

SISTEM IDENTIFIKASI MANUSIA DENGAN ANALISIS CIRI FISIS CITRA RUAS JARI (FINGER KNUCKLE) BERBASIS PENGOLAHAN CITRA DIGITAL

ABSTRAKSI: Salah satu teknologi yang dapat dijadikan solusi untuk menjaga keamanan dan kerahasiaan sebuah informasi yang telah teruji validitasnya adalah dengan menggunakan biometrik. Biometrik yang berbasis pada bentuk fisiologi dan karakteristik alami yang terdapat pada setiap manusia dapat digunakan untuk sistem identifikasi. Diantara berbagai karakteristik manusia dalam biometrik, metode identifikasi manusia melalui pola yang terdapat pada ruas jari memang belum banyak dikembangkan. Namun, metode ini tentu saja dapat digunakan untuk pengidentifikasian karena pola ruas jari pada setiap manusia memiliki keunikan dan karakteristik yang berbeda-beda, seperti hasil penelitian yang dilakukan oleh A. Kumar dan Ch. Ravikanth dalam papernya “Personal Authentication Using Finger Knuckle Surface” berhasil mengidentifikasi manusia berdasarkan pola pada ruas-ruas jari manusia tersebut.Pada Tugas Akhir ini dirancang dan dianalisis sebuah sistem untuk mengidentifikasi manusia menggunakan pola pada ruas jari tangan (finger knuckle). Citra jari yang digunakan adalah jari telunjuk, tengah, dan manis. Simulasi sistem ini dilakukan dengan bantuan perangkat (software) Matlab 2009a. Sampel yang diuji diakuisisi lalu diolah dengan berbasis pengolahan citra dan menggunakan metode Principal Component Analysis (PCA) untuk ekstraksi ciri, serta K-Nearest Neighbor untuk klasifikasi. Keluaran dari sistem ini berupa pengenalan pola ruas jari dan pengambilan keputusan yang tepat untuk setiap pola ruas jari yang menjadi masukan.Dengan menggunakan citra latih sebanyak 120 citra dan citra uji sebanyak 120 citra yang masing-masing berasal dari 30 orang, tingkat akurasi terbaik pada saat pengujian diperoleh dengan menggunakan 23 Principal Component, nilai k = 1, dan metode perhitungan cosine, yaitu sebesar 100%.Kata Kunci : identifikasi, ruas jari, pengolahan citra digital, PCA, K-Nearest NeighborABSTRACT: One of technology which can be a solution to maintain the security and confidentiality of information and has been proven its validity is use biometrics. Biometrics based on the shape of the physiology and natural characteristics every human being can be used for system identification. Among various human characteristics in biometrics, human identification method through pattern found on the knuckles is not developed widely yet. However, this method can certainly be used for identification because the pattern of knuckles on each human is unique and has different characteristics.In this final project the system was designed and analyzed to identify human using patterns of finger knuckles. Finger images that used are index, middle, and ring. The system is examined using Matlab 2009a as a helping tool. The testing sample is captured and is processed based on image processing, using Principal Component Analysis (PCA) method for feature extraction and K-Nearest Neighbor for identify. The output of this system is a decision of the identity of each finger knuckle.The best accuracy using 120 images for training and 120 images for testing is obtained with 23 Principal Components, the value of k = 1, and cosine calculation method, that is equal to 100%.Keyword: identification, finger knuckles, digital image processing, PCA, K-Nearest Neighbo

Fusion of geometric and texture features for finger knuckle surface recognition

AbstractHand-based biometrics plays a significant role in establishing security for real-time environments involving human interaction and is found to be more successful in terms of high speed and accuracy. This paper investigates on an integrated approach for personal authentication using Finger Back Knuckle Surface (FBKS) based on two methodologies viz., Angular Geometric Analysis based Feature Extraction Method (AGFEM) and Contourlet Transform based Feature Extraction Method (CTFEM). Based on these methods, this personal authentication system simultaneously extracts shape oriented feature information and textural pattern information of FBKS for authenticating an individual. Furthermore, the proposed geometric and textural analysis methods extract feature information from both proximal phalanx and distal phalanx knuckle regions (FBKS), while the existing works of the literature concentrate only on the features of proximal phalanx knuckle region. The finger joint region found nearer to the tip of the finger is called distal phalanx region of FBKS, which is a unique feature and has greater potentiality toward identification. Extensive experiments conducted using newly created database with 5400 FBKS images and the obtained results infer that the integration of shape oriented features with texture feature information yields excellent accuracy rate of 99.12% with lowest equal error rate of 1.04%

Determination of vitality from a non-invasive biomedical measurement for use in integrated biometric devices

Personal identification is a very important issue in today\u27s mobile and electronically networked societies. Among the available measures, fingerprints are the oldest and most widely used. Unfortunately, it is usually possible to deceive automatic fingerprint identification systems by presenting a well-duplicated synthetic or dismembered finger. This project is one method to provide fingerprint vitality authentication in order to solve this problem. Using a sensor that is composed of an array of capacitors, this method identifies the vitality of a fingerprint by detecting a specific changing pattern over the human skin. Mapping the two-dimensional images into one-dimensional signals, two ensembles of measures, namely static and dynamic measures, are used for classification. Static patterns as well as temporal changes in dielectric mosaic structure of the skin demonstrate themselves in these signals. Using these measures, this algorithm quantifies this specific pattern and makes a final decision about vitality of the fingerprint by a neural network trained by examples