Local Line Binary Pattern for Feature Extraction on Palm Vein Recognition

In recent years, palm vein recognition has been studied to overcome problems in conventional systems in biometrics technology (finger print, face, and iris). Those problems in biometrics includes convenience and performance. However, due to the clarity of the palm vein image, the veins could not be segmented properly. To overcome this problem, we propose a palm vein recognition system using Local Line Binary Pattern (LLBP) method that can extract robust features from the palm vein images that has unclear veins. LLBP is an advanced method of Local Binary Pattern (LBP), a texture descriptor based on the gray level comparison of a neighborhood of pixels. There are four major steps in this paper, Region of Interest (ROI) detection, image preprocessing, features extraction using LLBP method, and matching using Fuzzy k-NN classifier. The proposed method was applied on the CASIA Multi-Spectral Image Database. Experimental results showed that the proposed method using LLBP has a good performance with recognition accuracy of 97.3%. In the future, experiments will be conducted to observe which parameter that could affect processing time and recognition accuracy of LLBP is neede

Improved Local Line Binary Pattern (Illbp): An Improved Lbp-Based Biometric Descriptor For Face And Finger Vein Recognition

Face recognition under different illumination remains a challenging problem. The variations between the images of the same face due to illuminations are almost always being larger than image variations due to changes in face identity. For finger vein recognition, the recognition rate may be degraded due to low quality of finger vein images. This is because finger vein images are not always clear and can display irregular shadings. A theoretically simple, yet efficient technique, called Improved Local Line Binary Pattern (ILLBP) has been proposed in order to solve the problems. The descriptor can be used for both face and finger vein recognition. The effectiveness of the proposed technique is empirically demonstrated using Principal Component Analysis-k-Nearest Neighbor (PCA-kNN), Multiclass Support Vector Machine (Multiclass SVM) and Hamming Distance(HD) as the classifiers. Comparisons among other existing Local Binary Pattern (LBP) variants on the Yale Face Database B, Extended Yale Face Database B and our own finger vein database have been conducted. The advantages of our technique include higher accuracy compared to other LBP variants and fast computational time. The experimental results for face recognition showed that by using PCA-kNN, the best ILLBP (N = 15, P = 2) achieved a high recognition rate (89.24%) only slightly worse than the best LLBP with N = 17 (89.36%)

Real-Time Face Recognition System Using KPCA, LBP and Support Vector Machine

With increasing security threats, Biometric systems have importance in different fields. This appears clearly exactly after the rapid development that happened in power of computing. In this paper, the Design and implementation of a real-time face recognition system are presented. In such a system, Kernel principal component analysis (KPCA) and Local binary pattern (LBP) are used as feature extraction methods with the aid of support vector machine (SVM) to work as a classifier. A comparison between traditional feature extraction methods as (PCA and LDA) and a proposal methods are performed as well as a comparison between support vector neural network and artificial neural network classifier are also implemented. Two types of experiments, On-line, and Off-line experiments are done. In the On-line experiment, a new database is created and used. While in the off-line experiment, two types of databases (ORL and YALE) are used to estimate the performance and efficiency of the system. The combinations of these methods together enhances the experimental results in compare with other methods