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

Hybrid clustering based on multi-criteria segmentation for higher education marketing

Market segmentation in higher education institutions is still rarely applied although it can assist in defining the right strategies and actions for the targeted market. The problem that often arises in market segmentation is how to exploit the preferences of students as customers. To overcome this, the combination of hybrid clustering method with multiple criteria will be applied to the case of the market segmentation for students in higher education institutions. The integration of geographic, demographic, psychographic, and behavioral criteria from students is used to get more insightful information about student preference. Data result of the integration will be processed using hybrid clustering using K-means and self organizing map (SOM) algorithm. The hybrid clustering conducted to get promising clustering result along with the visualization of segmentation. This study successfully produces five student segments. It received 1,386 as the Davies-Bouldin index (DBI) value and 2,752 as the quantization error (QE) value which indicates a good clustering result for market segmentation. In addition, the visualization of the clustering result can be seen in a hexagonal map

Multi-feature Fusion Using SIFT and LEBP for Finger Vein Recognition

In this paper, multi-feature fusion using Scale Invariant Feature Transform (SIFT) and Local Extensive Binary Pattern (LEBP) was proposed to obtain a feature that could resist degradation problems such as scaling, rotation, translation and varying illumination conditions. SIFT feature had a capability to withstand degradation due to changes in the condition of the image scale, rotation and translation. Meanwhile, LEBP feature had resistance to gray level variations with richer and discriminatory local characteristics information. Therefore the fusion technique is used to collect important information from SIFT and LEBP feature.The resulting feature of multi-feature fusion using SIFT and LEBP feature would be processed by Learning Vector Quantization (LVQ) method to determine whether the testing image could be recognized or not. The accuracy value could achieve 97.50%, TPR at 0.9400 and FPR at 0.0128 in optimum condition.  That was a better result than only use SIFT or LEBP feature

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. 

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

Identifikasi Parameter Optimal dari Metode Fuzzy Co-Clustering dan Estimasi Robust Spasial pada Segmentasi Citra dengan Noise

Segmentasi citra merupakan salah satu kunci penting untuk proses analisis citra dan pengembangan berbagai aplikasi yang melibatkan pengolahan citra. Untuk melakukan segmentasi citra, telah banyak metode yang digunakan. Metode-metode tersebut antara lain segmentasi dengan histogram, segmentasi dengan deteksi tepi, segmentasi dengan Fuzzy C-Means (FCM) dan metode-metode lainnya. Adanya noise pada citra merupakan salah satu permasalahan yang sering ditemui dalam segmentasi citra. Sehingga diperlukan metode segmentasi citra yang mampu mengatasi pemasalahan tersebut. Pada Tugas Akhir ini, metode yang akan diimplentasikan dalam proses segmentasi citra berwarna adalah pengembangan algoritma Fuzzy C-Means yang disebut Fuzzy Co-Clustering For Images (FCCI) dan estimasi robust spasial untuk meningkatkan kemampuan segmentasi pada kasus citra berwarna dengan noise. Estimasi Robust Spasial merupakan metode yang digunakan untuk mengumpulkan informasi spasial dari citra sehingga dapat menyaring data dari noise dengan pendekatan statistik. Dengan estimasi robust spasial yang ditambahkan pada algoritma segmentasi citra yang digunakan diharapkan mampu memberikan hasil segmentasi yang baik terhadap inputan citra dengan noise.Dari hasil uji coba, metode ini memiliki nilai evaluasi kuantitatif yang kecil dan nilai PSNR yang tinggi untuk masukan citra berwarna dengan noise dengan rata-rata error color kurang dari 1.15 %. ==================================================================================================================== Image segmentation is one important key to the process of image analysis and development of a wide range of applications involving image processing. To perform image segmentation, has been widely used methods. These methods include the histogram segmentation, segmentation with edge detection, segmentation with Fuzzy C-Means (FCM) and other methods. The presence of noise in the image is one of the problems frequently encountered in image segmentation. So, we need a method of image segmentation that can overcome these problems. In this final project, the method is implemented in a process that will color image segmentation is the development of Fuzzy C-Means algorithm called Fuzzy Co-Clustering For Images (FCCI) and robust estimation to improve the ability of spatial segmentation in the case of color images with noise. Robust Estimation of Spatial is the method used to collect spatial information of the image so that it can filter out noise in the data from a statistical approach. With a robust estimate of the added spatial image segmentation algorithm that is used is expected to provide good segmentation results of the input image with noise. Based on the result of experiments, this method has little quantitative evaluation value and high PSNR value for input color image with noise, this method has average error color less than 1,15 %