LEARNING-FREE DEEP FEATURES FOR MULTISPECTRAL PALM-PRINT CLASSIFICATION

The feature extraction step is a major and crucial step in analyzing and understanding raw data as it has a considerable impact on the system accuracy. Unfortunately, despite the very acceptable results obtained by many handcrafted methods, they can have difficulty representing the features in the case of large databases or with strongly correlated samples. In this context, we proposed a new, simple and lightweight method for deep feature extraction. Our method can be configured to produce four different deep features, each controlled to tune the system accuracy. We have evaluated the performance of our method using a multispectral palmprint based biometric system and the experimental results, using the CASIA database, have shown that our method has high accuracy compared to many current handcrafted feature extraction methods and many well known deep learning based methods

Handbook of Vascular Biometrics

This open access handbook provides the first comprehensive overview of biometrics exploiting the shape of human blood vessels for biometric recognition, i.e. vascular biometrics, including finger vein recognition, hand/palm vein recognition, retina recognition, and sclera recognition. After an introductory chapter summarizing the state of the art in and availability of commercial systems and open datasets/open source software, individual chapters focus on specific aspects of one of the biometric modalities, including questions of usability, security, and privacy. The book features contributions from both academia and major industrial manufacturers

Authentication technology methods for E-Commerce applications in Nigeria — a case for biometric digital security contactless palm vein authentication

E-Commerce has become one of the most interesting and beneficial Internet-enabled systems for humanity. E-Commerce has served as an economic enabler and driver for developed countries because of the total adoption by their citizens. However, in Nigeria citizens have rejected E-Commerce due to a lack of trust and inadequate security. This research identifies several factors that lead to distrust of E-Commerce systems in Nigeria. These factors: perceived fear, security, perceived risk, trust, usability, perceived advantage, and use of web assurance seal services are very important for intention to adopt E-Commerce as an online transaction technology. This thesis uses a novel Design Fiction and E-Commerce website simulation methodology to show citizens how new and improved security in E-Commerce could increase those citizens' trust and thus increase their intention to adopt E-Commerce. The research surveys a broad demographic sample of citizens from Nigeria who completed a set of tasks associated with the novel Design Fiction and E-Commerce website simulation followed by a detailed questionnaire. The questionnaire, with associated items, was designed to answer the research questions and hypothesis based on the E-Commerce Adoption Model proposed in the thesis. This new E-Commerce Adoption model is based on the Technology Acceptance Model and uses to comparatively test Digital Signature, Finger Print Identification, and Contactless Palm Vein Authentication technologies in E-Commerce transactions. Results from the survey show that Contactless Palm Vein Authentication leads to greater trust in E-Commerce in Nigeria. The thesis research findings also indicate that new improved security authentication techniques are overdue. The research indicates that poor E-Commerce adoption in Nigeria is mainly due to a key identified factor, which is security. The conceptual model and trust model are developed for E-Commerce adoption in Nigeria. Therefore, it shows that citizens are willing to accept Contactless Palm Vein Authentication as a solution. In particular, the research results also show that there are strong relationships between all the identified factors and citizens’ intention to adopt E-Commerce in Nigeria thus rejecting all null hypotheses