QUALITY-BASED RANK FUSION METHOD

Quality-based rank fusion method depends not only on the ranking list of the unimodal classifiers, but also on the quality of the input images. Usually, this method applies on other biometric rank fusion approach with the modification by incorporating the quality of the input image. Quality based fusion methods usually do not have any training phase and hence can be used in other biometric information fusion process, such as fuzzy logic based fusion. There is no specific rule or general equation for quality based fusion method. Researchers can apply this method to any of their existing methods to improve the identification or verification rate. For example, Abaza and Ross introduced a quality based rank fusion method by modifying the exist.

Different existing methodologies for rank level fusion methods for multimodal, biometric system have been reviewed. The methods for rank level fusion include plurality voting method, highest rank method, Borda count method, logistic regression method, and quality-based rank fusion method. Advantages and disadvantages of all of these rank fusion methods have been discussed in the context of current state of the art in the discipline. Also, with the help of appropriate diagrams, outcomes of different possible rank fusion methods have been shown. In the next chapter, a new rank fusion method, the Markov chain based rank fusion method will be discussed which has several advantages over the traditional rank fusion methods.

Before we discuss each of the security tools, we need to briefly mention that all tools and policies are meant to address one or more core security goals, which are known as CIA – Confidentiality, Integrity, and Availability.

   Confidentiality involves making sure that only authorized individuals can access information or data. Integrity involves making sure that data are consistent and complete. For example, as a message is transmitted, its content is not modified unwillingly during the transmission. Finally, availability involves ensuring that system and/or data are available when they are needed. For systems to be considered highly available, the organization must protect them from disruptions not only due to security threats such as denial of service attacks, but also due to power outages, hardware failures, and system upgrades.