Technology Gap Navigator: Emerging Design of Biometric-Enabled Risk Assessment Machines

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

Shmerko

2020

Self Cite

This paper addresses the evaluation of the performance of the decision support system that utilizes face and facial expression biometrics. The evaluation criteria include risk of error and related reliability of decision, as well as their contribution to the changes 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 that demonstrate the phenomenon of biases in biometrics that affect the evaluated measures of the performance in human-machine systems.

Section: Cross Spectral Biometric Traitsmentioning

confidence: 99%

“…The change in testing procedures results in a less reliable system. The computation of Bias Trust can be used in combination with the Technology Gap Navigator [12] to calculate the trust in a system when the base condition is modified, such as the deployment in a different environment.…”

Section: B Subject Identificationmentioning

confidence: 99%

Reliability of Decision Support in Cross-spectral Biometric-enabled Systems

Lai

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

Shmerko

2020

Self Cite

“…In this paper, we focus on capturing the change in the degree of Trust as it is a critical task in real-world applications. As described in [20], the performance in a simulated environment is wildly different from real-world applications. It should be noted that the reliability matrices can be constructed in order to help identify the biases within the system and instead of constructing the Reliability matrices directly, we provide the accuracy measures in Table IV and V. The construction and analysis of the Reliability matrices is out of scope of this paper.…”

Section: Trust Risk and Reliabilitymentioning

confidence: 99%

Decision Support for Video-based Detection of Flu Symptoms

Lai

2020

Preprint

The development of decision support systems is a growing domain that can be applied in the area of disease control and diagnostics. Using video-based surveillance data, skeleton features are extracted to perform action recognition, specifically the detection and recognition of coughing and sneezing motions. Providing evidence of flu-like symptoms, a decision support system based on causal networks is capable of providing the operator with vital information for decision-making. A modified residual temporal convolutional network is proposed for action recognition using skeleton features. This paper addresses the capability of using results from a machine-learning model as evidence for a cognitive decision support system. We propose risk and trust measures as a metric to bridge between machinelearning and machine-reasoning. We provide experiments on evaluating the performance of the proposed network and how these performance measures can be combined with risk to generate trust.

“…4) and the inference engine can be used for estimation of technology gaps. This is required to identify whether it is possible to achieve the design goals given the available resources and technologies [21]. The focus of our future work is the cognitive biases in machine reasoning in a multi-state identity management process.…”

Section: )mentioning

confidence: 99%

Cognitive Identity Management: Risks, Trust and Decisions using Heterogeneous Sources

2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI)

Howells

Crockett

et al. 2019

This work advocates for cognitive biometric-enabled systems that integrate identity management, risk assessment and trust assessment. The cognitive identity management process is viewed as a multi-state dynamical system, and probabilistic reasoning is used for modeling of this process. This paper describes an approach to design a platform for risk and trust modeling and evaluation in the cognitive identity management built upon processing heterogeneous data including biometrics, other sensory data and digital ID. The core of an approach is the perception-action cycle of each system state. Inference engine is a causal network that uses various uncertainty metrics and reasoning mechanisms including Dempster-Shafer and Dezert-Smarandache beliefs.