The network of blood vessels possesses several properties that make a good biometric feature for personal identification: (1) they are difficult to damage and modify; (2) they are difficult to simulate using a fake template; and (3) vein information can represent the liveness of the person. In the process of recognition of the network of blood vessels, we encounter two main difficulties: the first difficulty concerns the enhancement of the image of blood vessels obtained from the camera working in visible and/or infrared light, and the second one concerns the process of extraction of features and methods of classification. In the first part, this chapter presents the basic methods of preprocessing biometric images. In the second part, we discuss the process of feature extraction with particular emphasis on the feature extraction from images depicting the network of blood vessels. This applies to texture analysis using the co-occurrence matrix, Gabor filtration, moments, and topological features using cross points. In the third part, we present the methods of processing images of the blood vessel network of dorsal part of the hand and wrist. We also discuss the process of reducing the dimensionality of a feature vector using the principal components analysis method.
Part of the book: Machine Learning and Biometrics
Unimodal biometric systems have limited effectiveness in identifying people, mainly due to their susceptibility to changes in individual biometric features and presentation attacks. The identification of people using multimodal biometric systems attracts the attention of researchers due to their advantages, such as greater recognition efficiency and greater security compared to the unimodal biometric system. To break into the biometric multimodal system, the intruder would have to break into more than one unimodal biometric system. In multimodal biometric systems: The availability of many features means that the multimodal system becomes more reliable. A multimodal biometric system increases security and ensures confidentiality of user data. A multimodal biometric system realizes the merger of decisions taken under individual modalities. If one of the modalities is eliminated, the system can still ensure security, using the remaining. Multimodal systems provide information on the “liveness” of the sample being introduced. In a multimodal system, a fusion of feature vectors and/or decisions developed by each subsystem is carried out, and then the final decision on identification is made on the basis of the vector of features thus obtained. In this chapter, we consider a multimodal biometric system that uses three modalities: dorsal vein, palm print, and periocular.
Part of the book: Security and Privacy From a Legal, Ethical, and Technical Perspective