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The next-generation PASS is a multimode biometric system that exploits all possible aspects of biometric data. We show how to utilize discriminative properties of distance biometrics. A natural property of the PASS is the alleviation of temporary faults of biometric devices caused by poorly environment (illumination and motions). In the PASS, uncertainty in automatic decision-making at the local level (biometric device) is not transferred to the global level. This is because this uncertainty is transformed into questions that must be clari ed by the of cer. Hence, the PASS is a unique platform for the deployment of biometric sensors with intelligent support called a biometric assistant. The use of various biometrics, including early detection and warning devices in the system, must involve innovative approaches, sociological, and economic considerations. The proposed PASS is considered to be a unique area to combine and prototype various methodologies, concepts, and techniques from many areas of the natural and social sciences in particular, image processing and pattern recognition, virtual environment design and synthetic biometric data generation, distributed and multiagent systems design, human machine interaction, integrated knowledge intensive system design, communication, medicine, and psychology. We introduced the fundamentals of the next generation of PASS and T-PASS design. These are speci c-area, multidisciplinary applications that require the utilization of advanced methodologies and techniques from many areas; in other words, they require a multidisciplinary approach. The speci c area applications, such as PASS and T-PASS, form a suitable basis for systematic development in research laboratories and for industrial development. Our study contributes to the development of the following design concepts: 1. Aggregative biometric-based PASS. In this chapter, we introduce the basic con guration (platform) with an extension using the early warning paradigm. A further extension can also be achieved by deploying various biometric subsystems to pre-screened and screened individuals, and using supporting tools. Utilization of the aggregative property addresses the progress of biometric sensors. The above addresses the open PASS architecture. 2. Recon gurable biometric-based PASS. Using the proposed basic con guration, the PASS can be recon gured into a training system, T-PASS. To this end, cost-ef cient extensions such as additional simulators of synthetic biometric data, are utilized. 3. Distributed topology for the PASS. This topology involves pre-screening and screening areas, typically for airport checkpoints, facilities access, and so on. Such a distributed topology provides the conditions for the implementation of a situational awareness paradigm. 4. Mobile biometric-based PASS. To achieve this new operational characteristic, several design techniques, such as modularity and self-testing at local levels, can be utilized. In our design concept, the PASS is a mobile screening checkpoint. This function can be useful in many applications for example, special event or VIP security. Many important public or high-pro le settings
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face a host of challenges in security, because they lack exible screening options or need the most sophisticated security checkpoint solutions. The training system, T-PASS, inherits the mobility property as well, so personnel can be ef ciently trained in a new environment, wherever the PASS is deployed. 5. Decision-support assistants. Our study on hyperspectral observation of the pre-screened and screened person based on facial skin texture is a particular example of such the capabilities of an assistant. We also present the results of prototyping various decision-support assistants based on noninvasive temperature measurement. The introduced approach also contributes to the development of techniques for intelligent decision-making in the biometric-based PASS, in the following directions: (a) Biometric data understanding. The support of security personnel is implemented in semantic form that is, in the form of automatically generated questions recommended for the personnel to be used in dialogue with the customers. To accomplish this, we have developed a method for transferring biometric features (information in the feature domain) into a semantic representation, and then into a set of questions (information in the knowledge domain). The approach uses techniques of image understanding and humanmachine interactions. Thus, decisions from the local level (biometric devices) are propagated to the global level (of cer). (b) Dealing with temporal faults of biometric sensors through the fusion at the decision-making level. Based on the fundamental properties of semiautomatic systems (where security personnel receive awareness data in support of their decision) and semantic representations, we developed a technique that alleviates temporal faults of biometric sensors. (c) Fusion of biometric data at the semantic level. This is a new approach to the integration of biometric data evaluated in semantic form. Compared to a classical correlation in the feature domain, fusion in knowledge domain can be applied to any biometric.
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