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Human machine and human human interactions are the key components of the PASS and T-PASS. In our design approach, the goal of the human machine interface is to support the of cer by providing an analysis of all sources of information in a form that is acceptable for fast and correct decision-making on authorization. The following static and dynamic (real-time) information streams provided by various tools can be identi ed: (a) static document record and biometric data from local and global databases and (b) online biometric data obtained from the surveillance facilities and conversation with an individual at the desk, including behavioral biometric data such as voice, facial expressions, and signature. Biometrics are usually classi ed using physiological and behavioral categories. In our design concept, we also distinguish contact and noncontact biometrics. This division is made with respect to the main functions of the PASS (early warning and service based on the screening discipline). Early warning information includes various parameters that can be obtained by indirect techniques (disabilities, drug and alcohol intoxication, etc.). The PASS is a multimodal biometric system using a combination of various biometrics, including visual-band, infrared, and acoustic data for identi cation of both appearance (including natural aspects, such as aging, and intentional ones, such as surgical changes), physiological characteristics (temperature, blood ow rate, etc.), and behavioral features (voice and gate) (Figure 24.3). Other biometrics can be used at pre-screening and checkpoints.
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Relationship of Biometric Data in Semantic
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The relationships between biometric data are another source of information in the PASS. In particular, similarities can be measured and expressed in terms of association, resemblance, correlation, and matching. In similarity measures, various basic principles can be utilized: heuristic, probabilistic, information-theoretical, fuzzy, semantic, and so on. Figure 24.4 illustrates the relationship between biometric features and human physiological and psychological characteristics. Biometrics represented by a feature vector is considered in multimodal systems in the form of concatenation into a single feature vector [19]. A feature-level fusion is done using matching scores and the corresponding rules. In our approach, information
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24
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Relationship
Temperature Alcohol and drugs consumption Blood pressure Pulse Artificial accessories Plastic surgery
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Tension Intonation Phonetic level Phonological level Morphological level Relationship with physiological parameters
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Facial expressions Iris Brow and lips movements Relationship with speech and physiological parameters
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Information source from 70-600Hz Information source from 3-12mm Authorized individual Information source from 400-700mm
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Figure 24.3. Relationship of biometric data in various spectral bands.
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from biometric devices is transformed into a semantic form (knowledge domain), and a relationship among various biometrics is integrated through a decision-level fusion. This relationship in semantic form is used to provoke the process of extraction of information from other sources using the questionnaire technique. This technique has been developed, in particular, for a polygraph [20]. A function similar to the objective function of polygraph can be used to extract additional information from the acoustic band and the questionnaire (information from answers and behavioral biometric data from the voice).
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Relationship Voice patterns Facial features Gait patterns
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Indirect Physiological characteristics condition
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Figure 24.4. Example of the indirect evaluation of human physiological and psychological
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characteristics, and their relationships to various biometrics.
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24.3 Decision-Making Support Assistant Design
24.3 DECISION-MAKING SUPPORT ASSISTANT DESIGN
The devices gathered from the sensors and intelligent data processing for the situational awareness are called decision-making support assistants. These assistants can be based on noninvasive metrics such as r Temperature measurement r Arti cial accessory detection r Estimation of drug and alcohol intoxication r Estimation of blood pressure and pulse The basic design paradigm of these decision support assistances is the discriminative biometrics.
Discriminative Biometrics
We introduce several examples of discriminative biometrics using infrared thermography from medical applications a diagnostic method that provides information about normal and abnormal functioning of the sensory and sympathetic nervous system, vascular dysfunction, myofascial trauma, and local in ammatory process. In prototyping the PASS, we studied various methods in order to choose the most ef cient one. In this section, we review some of them. An infrared image analysis component has been integrated into the prototyped PASS. It includes the recording of infrared image video, infrared image processing, and an analysis of features such as temperature and blood ow rate. The uctuation of temperature in various facial regions is primary due to the changing blood ow rate. In reference 21, the heat-conduction formulas at the skin s surface are introduced. The thermodynamic relation between the blood ow rate VS at the skin level, blood temperature at the body core Tblood , and the skin temperature Tskin is used to convert infrared intensity to temperature. Then, the raw thermal data are transformed into blood ow rate data. In medical applications, infrared-based diagnostic systems provide accurate quantitative analysis of the temperature distribution on a target surface: the absolute and mean temperature of any region in particular, of the face, and differences between the right and the left sides of the face. In reference 22, it was found that infrared thermography can be used as a screening test for distinguishing healthy subjects from patients with temporomandibular disorder. This result is used in the prototype PASS to detect the maximal and mean facial and neck region temperatures of an individual. In reference 23, mass blind screening of potential SARS or bird u patients was studied. In the above study, a handheld radiometric infrared ThermaCAM S60 FLIR