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

• Main Author: Chaiwuh, Shing
• Format: Thesis
• Language: English
• Published: 2013
• Subjects: TK1-9971 Electrical engineering. Electronics. Nuclear engineering
• Online Access: http://eprints.usm.my/45063/1/Chaiwuh%20Shing24.pdf; http://eprints.usm.my/45063/

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%).