A NOVEL PERSONAL AUTHENTICATION USING KNUCKLE MULTISPECTRAL PATTERN

ABSTRACT With the increased use of biometrics for identity verification, there have been similar increases in the use of unimodal biometric system. The finger knuckle print recognition is one of the newest biometric techniques research today. In this paper, one of the reliable and robust personal identification approaches using finger knuckle print is presented. Many researchers are going on in face, finger print and iris recognition and which finds its usage in many applications. These biometric which find its usage in many applications are easily duplicated for fraudulent activities. But the finger knuckle print recognition is the unique pattern to identify the individuality at a high level of accuracy. This paper proposes new algorithms for finger knuckle print recognition using SIFT algorithm and this algorithm presents, extracting a new original constant features from images As the proposed method matches the different angles of finger knuckle print with the database, its reliability is very high when compared to other biometrics. The features of SIFT which are invariant to image scale and rotation, are shown to provide robust matching across a substantial range of fine distortion, change in 3D viewpoint, addition of noise, and change in illuminance. The features are highly distinctive, in the sense that a single feature could be correctly matched with high probability against a large database of features from many images

A Robust Finger Knuckle Print Authentication using Topothesy and fractal dimension

This paper presents the finger knuckle based biometric authentication system using the approaches like structure entropy, GSHP (Gaussian smoothed High pass), GSOD (Gaussian Smoothed Oriented Directives) and also the well known method for surface roughness measurement called the fractal profiles represented by Topothesy and fractal dimension which describe  not only the roughness but also the affine self similarity. We have also implemented Daisy descriptor for the representation of texture. The results of fractal parameters along with the refined scores are comparable to those of the compcode and impcompcode

Sistem Biometrik Finger Knuckle Print (FKP) Menggunakan Metode Band-Limited Phase-Only Correlation (BLPOC)

ABSTRAKSI: Personal authentication berkembang cepat dalam kurun waktu 3 dekade setelah ditemukannya sistem biometrik. Sistem Biometrik dinilai lebih efektif untuk keperluan personal authentication karena melekat pada tubuh, tidak dapat dipindahtangankan dan cenderung sulit untuk ditiru. Salah satu region biometrik yang baru dikembangkan adalah buku jari atau finger knuckle.Band-Limited Phase-Only Correlation (BLPOC) adalah metode pengembangan dari POC yang dapat mencari derajat kesamaan dari kedua citra yang diuji. Dalam metode POC sendiri terdapat Fourier Transfrom yang merubah nilai pada piksel citra dari domain waktu menjadi domain frekuensi. Setelah itu, kedua citra kemudian digabung menghasilkan cross-phase spectrum, terakhir cross-phase spectrum dinormalisasi sehingga diketahui derajat kesamaan antar 2 citra, ditunjukkan sebagai peak. Sedangkan pada metode BLPOC frekuensi tinggi tidak diikutsertakan dalam perhitungan.Penelitian ini menggunakan 400 citra FKP (Finger Knuckle Print) greyscale yang terbagi menjadi 40 user dimana setiap user memiliki 10 data citra FKP. Setelah dilakukan pengujian dan analisis ternyata metode BLPOC cukup berpotensi untuk dipakai sebagai salah satu metode pada sistem biometrik dimana nilai akurasi mencapai 91,67% untuk kasus citra genuine dan 96,84% untuk kasus citra imposter.Kata Kunci : sistem biometrik, Finger Knuckle Print, Band-Limited Phase-Only Correlation, Fourier Transform, peak.ABSTRACT: Personal authentication has grown rapidly in the past 3 decades after biometric system discovery. Biometric system is considered more effective for the purposes of personal authentication as its attached to the body, non-transferable and tend to be difficult to replicate. One region that newly developed in biometric system is finger knuckle.Band-Limited Phase-Only Correlation (BLPOC) is the development of the POC method that can find the degree of similarity between the two images that are tested. POC method itself are Fourier Transform that converts the image pixel value from time domain to frequency domain. After that, the two images are combined producing cross-phase spectrum, then cross-phase spectrum normalized revealing final result known as degree of similarity between two images, shown as a peak. While high frequencies in BLPOC methods are not included in the calculation.This study uses 400 greyscale FKP (Finger Knuckle Print) images, divided into 40 users where each user has 10 FKP images data. After testing and analyzing methods BLPOC apparently has enough potential to be used in biometric system, reaching 91.67% accuracy for genuine image cases and 96.84% for imposter image cases.Keyword: biometric system, Finger Knuckle Print, Band-Limited Phase-Only Correlation, Fourier Transform, peak

Finger-Knuckle-Print Verification Based on Band-Limited Phase-Only Correlation

13th International Conference on Computer Analysis of Images and Patterns, CAIP 2009, Munster, 2-4 September 2009This paper investigates a new automated personal authentication technique using finger-knuckle-print (FKP) imaging. First, a specific data acquisition device is developed to capture the FKP images. The local convex direction map of the FKP image is then extracted, based on which a coordinate system is defined to align the images and a region of interest (ROI) is cropped for feature extraction and matching. To match two FKPs, we present a Band-Limited Phase-Only Correlation (BLPOC) based method to register the images and further to evaluate their similarity. An FKP database is established to examine the performance of the proposed method, and the promising experimental results demonstrated its advantage over the existing finger-back surface based biometric systems.Department of ComputingRefereed conference pape

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%

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

PENERAPAN PRINCIPAL COMPONENT ANALYSIS (PCA) DAN BACKPROPAGATION NEURAL NETWORK (BPNN) UNTUK IDENTIFIKASI FINGER KNUCKLE PRINT BERBASIS ANDROID

Finger Knuckle Print (FKP) merupakan biometrik dengan pola kulit kaya tekstur, terlihat jelas dan tidak mudah terkelupas, sehingga dapat digunakan sebagai identifikasi biometrik bersifat contactless. Kelebihan FKP tersebut dapat menutupi kelemahan dari sidik jari sulit dikenali dan rentan phising. Pada penelitian ini diterapkan pengolahan citra digital dengan Principal Component Analysis (PCA) untuk ekstraksi ciri FKP dan jaringan syaraf tiruan dengan Backpropagation Neural Network (BPNN) untuk training dan identifikasi FKP. Citra FKP yang digunakan berukuran 40 x 40 piksel setelah dilakukan resize, variabel inputan BPNN berjumlah K diperoleh dari hasil ekstraksi ciri PCA dengan output adalah hasil identifikasi berupa id dan nama pemilik. Data FKP diperoleh secara langsung dari 10 mahasiswa sebanyak 100 data, dengan pembagian (90:10) 9 citra latih dan 1 citra uji masing-masing mahasiswa. Pengujian dilakukan adalah whitebox, nilai K dan parameter BPNN dengan auto dan non-auto treshold. Nilai K yang digunakan adalah 9 dan 20, Parameter BPNN yang digunakan adalah maksimum epoch 100.000,learning rate (α) 0,09; 0,05; 0,01 dan target error 0,001 dengan fungsi aktivasi linear. Berdasarkan hasil penelitian menghasilkan akurasi rata-rata 100% dengan auto treshold dan 48,33% dengan non-auto treshold. Dengan demikian penerapan PCA dan BPNN tepat diimplemantasikan untuk kasus identifikasi FKP berbasis android. Kata Kunci: android, finger knuckle print, backpropagation, principal component analysi