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

• URL: http://arxiv.org/abs/2008.05735v1
• Date: Thu, 13 Aug 2020 07:43:14 GMT
• Title: Reliability of Decision Support in Cross-spectral Biometric-enabled Systems
• Authors: Kenneth Lai, Svetlana N. Yanushkevich, and Vlad Shmerko
• Abstract summary: This paper addresses the evaluation of the performance of the decision support system that utilizes face and facial expression biometrics. The relevant applications include human behavior monitoring and stress detection in individuals and teams, and in situational awareness system.
• Score: 2.278720757613755
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• A Human-Centered Risk Evaluation of Biometric Systems Using Conjoint Analysis [0.6199770411242359]
  This paper presents a novel human-centered risk evaluation framework using conjoint analysis to quantify the impact of risk factors, such as surveillance cameras, on attacker's motivation. Our framework calculates risk values incorporating the False Acceptance Rate (FAR) and attack probability, allowing comprehensive comparisons across use cases.
  arXiv  Detail & Related papers  (2024-09-17T14:18:21Z)
• Long-Range Biometric Identification in Real World Scenarios: A Comprehensive Evaluation Framework Based on Missions [11.557368031775717]
  This paper evaluates research solutions for identifying individuals at ranges and altitudes. By fusing face and body features, we propose developing robust biometric systems for effective long-range identification.
  arXiv  Detail & Related papers  (2024-09-03T02:17:36Z)
• Multimodal Adaptive Fusion of Face and Gait Features using Keyless attention based Deep Neural Networks for Human Identification [67.64124512185087]
  Soft biometrics such as gait are widely used with face in surveillance tasks like person recognition and re-identification. We propose a novel adaptive multi-biometric fusion strategy for the dynamic incorporation of gait and face biometric cues by leveraging keyless attention deep neural networks.
  arXiv  Detail & Related papers  (2023-03-24T05:28:35Z)
• A Survey on Computer Vision based Human Analysis in the COVID-19 Era [58.79053747159797]
  The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals. Various prevention measures were introduced around the world to limit the transmission of the disease, including face masks, mandates for social distancing and regular disinfection in public spaces, and the use of screening applications. These developments triggered the need for novel and improved computer vision techniques capable of (i) providing support to the prevention measures through an automated analysis of visual data, on the one hand, and (ii) facilitating normal operation of existing vision-based services, such as biometric authentication
  arXiv  Detail & Related papers  (2022-11-07T17:20:39Z)
• Anatomizing Bias in Facial Analysis [86.79402670904338]
  Existing facial analysis systems have been shown to yield biased results against certain demographic subgroups. It has become imperative to ensure that these systems do not discriminate based on gender, identity, or skin tone of individuals. This has led to research in the identification and mitigation of bias in AI systems.
  arXiv  Detail & Related papers  (2021-12-13T09:51:13Z)
• Biometrics: Trust, but Verify [49.9641823975828]
  Biometric recognition has exploded into a plethora of different applications around the globe. There are a number of outstanding problems and concerns pertaining to the various sub-modules of biometric recognition systems.
  arXiv  Detail & Related papers  (2021-05-14T03:07:25Z)
• Decision Support for Video-based Detection of Flu Symptoms [3.0255457622022486]
  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 machine-learning and machine-reasoning.
  arXiv  Detail & Related papers  (2020-08-24T16:16:38Z)
• Risk, Trust, and Bias: Causal Regulators of Biometric-Enabled Decision Support [6.32220198667533]
  Risk, trust, and bias (R-T-B) are emerging measures of performance of such systems. This paper offers a complete taxonomy of the R-T-B causal performance regulators for the biometric-enabled DSS. The proposed novel taxonomy links the R-T-B assessment to the causal inference mechanism for reasoning in decision making.
  arXiv  Detail & Related papers  (2020-08-05T20:49:13Z)
• Assessing Risks of Biases in Cognitive Decision Support Systems [5.480546613836199]
  This paper addresses a challenging research question on how to manage an ensemble of biases? We provide performance projections of the cognitive Decision Support System operational landscape in terms of biases. We also provide a motivational experiment using face biometric component of the checkpoint system which highlights the discovery of an ensemble of biases.
  arXiv  Detail & Related papers  (2020-07-28T16:53:45Z)
• Opportunities of a Machine Learning-based Decision Support System for Stroke Rehabilitation Assessment [64.52563354823711]
  Rehabilitation assessment is critical to determine an adequate intervention for a patient. Current practices of assessment mainly rely on therapist's experience, and assessment is infrequently executed due to the limited availability of a therapist. We developed an intelligent decision support system that can identify salient features of assessment using reinforcement learning.
  arXiv  Detail & Related papers  (2020-02-27T17:04:07Z)
• Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions [48.91284724066349]
  Off-policy evaluation in reinforcement learning offers the chance of using observational data to improve future outcomes in domains such as healthcare and education. Traditional measures such as confidence intervals may be insufficient due to noise, limited data and confounding. We develop a method that could serve as a hybrid human-AI system, to enable human experts to analyze the validity of policy evaluation estimates.
  arXiv  Detail & Related papers  (2020-02-10T00:26:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.