The purpose of this paper is to provide a biometric based technology roadmap to advance personalized Augmentative and Alternative Communication (AAC) systems for individuals with disabilities. The proposed technology roadmap introduces two core components: an AAC biometric register and interoperable technological modules. The biometric register provides a structured framework for capturing and transforming physiological and behavioral traits to enable adaptive and context-aware communication. The interoperable module design supports reconfigurable AAC architectures, ensuring compatibility across diverse interfaces and devices. Together, these mechanisms establish a foundation for automated and scalable personalization in AAC technologies. The study demonstrates that the proposed methodology for technology roadmapping effectively connects established research with emerging computational practices. This is confirmed by the results of the case study such as sign language recognition. • Personalized Augmentative and Alternative Communication (AAC) is a branch of assistive technologies for people with communication disabilities. Technology roadmapping that adheres to the best biometric practices is a step toward future AAC technologies. • A biometric register is a framework for AAC. It links biometric traits, such as gestures, to intermediate traits, such as synthesized speech, for customizable communication channels. AAC provides accessibility through open protocols and modular design. The methodology for AAC technology roadmapping includes the developed expert elicitation protocol.

The goal of this paper is to audit the applications and trends of the state-of-the-art Computational Intelligence (CI) as an assistive technology for Equity, Diversity, and Inclusion (EDI). To accomplish the audit objective, a roadmapping mechanism was developed and used for the analysis of the CI approaches that were applied and show promise for the research and development of the EDI-aware systems. The audit identifies the strategic CI reserve, comprised of machine reasoning techniques such as probabilistic causal graph models, Granger causal model, and advanced statistical approaches.

This paper addresses the evaluation of the performance of face and facial expression biometrics through a decision support system. The evaluation criteria include capturing the risk of the system, estimating the reliability of decision, and predicting the change in the perceived operator's trust in the decision. The relevant applications include human behavior monitoring and stress detection in individuals and teams, and in situational awareness system. Using an available database of cross-spectral videos of faces and facial expressions, we conducted a series of experiments to 1) demonstrate the phenomenon of biases in biometrics that affect the evaluated measures of the performance in human-machine systems, 2) explore the overall risk of the system caused by error rates such as false match and false nonmatch rates, and 3) calculate the reliability of cross-spectral and emotion-varying face identification.

This paper is concerned with mass evidence accumulation and risk assessment in a particular component of transportation systems and e-borders. We outline the challenges faced by contemporary border control technology and conduct a series of demonstrative experiments that cover critical scenarios, tasks, and states of both evidence accumulation and the traveler risk scoring engine. Using technology gap navigator methodology, this paper suggests an approach to traveler risk estimation based on a unified inference platform, such as a causal graphical model with various incorporated metrics of uncertainty.

This paper concerns with facial-based watch list technology as a component of automated border control machines deployed in e-borders. The key task of the watch list technology is to mitigate effects of mis-identification and impersonation. To address this problem, we developed a novel cost-based model of traveler risk assessment and proved its efficiency via intensive experiments using large-scale facial databases. The results of this study are applicable to any biometric modality to be used in watch list technology.

This paper revisits the concept of an authentication machine (A-machine) that aims at identifying/verifying humans. Although A-machines in the closed-set application scenario are well understood and commonly used for access control utilizing human biometrics (face, iris, and fingerprints), open-set applications of A-machines have yet to be equally characterized. This paper presents an analysis and taxonomy of A-machines, trends, and challenges of open-set real-world applications. This paper makes the following contributions to the area of open-set A-machines: 1) a survey of applications; 2) new novel life cycle metrics for theoretical, predicted, and operational performance evaluation; 3) a new concept of evidence accumulation for risk assessment; 4) new criteria for the comparison of A-machines based on the notion of a supporting assistant; and 5) a new approach to border personnel training based on the A-machine training mode. It offers a technique for modeling A-machines using belief (Bayesian) networks and provides an example of this technique for biometric-based e-profiling.

The above paper finds an optimal fixed-polarity Reed-Muller expansion of an n-variable totally symmetric function using an OFDD-based algorithm that requires O(n/sup 7/) time and O(n/sup 6/) storage space. However, an algorithm based on Suprun's transient triangles requires only O(n/sup 3/) time and O(n/sup 2/) storage space. An implementation of this algorithm yields computation times lower by several orders of magnitude.