Fusion of Palmprint, Palm Vein and Dorsal Hand Vein for Personal Identification

Security is one of the major concerns of human beings in the 21st century. Many forensic and governmental sections now have trusted biometric systems to provide high levels of security for them. Lots of researchers have also worked on many different biometric modalities to both ensure the security and the convenience of the end-users. Nowadays, concerning the magnificent potentials of hand based biometrics, they are a trending choice for a wide range of applications since it is commonly accepted by the society and is not considered to be intrusive while it can offer plenty of features that are abundant to identify humans on a large scale. This thesis uses three different hand-based biometric modalities, namely palmprint, palm vein, and dorsal hand vein to create a secure, efficient, and accurate multimodal hand-based biometric system. Additionally, four different feature extraction methods, namely Principal Component Analysis (PCA), Local Binary Patterns (LBP), Scale Invariant Feature Transforms (SIFT) and Speeded-Up Robust Features (SURF), are exploited to perform person identification. Experiments are conducted on the CASIA palmprint database, Tongji palm vein database, and Bosphorus dorsal vein database. Unimodal and multimodal experimental results are presented on all databases. Moreover, we propose a new multimodal method on palmprint, palm vein, and dorsal hand vein biometrics employing Feature-Level Fusion and Decision-Level Fusion techniques. Finally, the results are presented on six different datasets obtained from the aforementioned palmprint, palm vein, and dorsal vein databases. Keywords: Person Identification, Biometrics, Palmprint Biometrics, Palm Vein Biometrics, Dorsal Vein Biometrics, Information Fusion.