5 research outputs found
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
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.