Fairness on Synthetic Visual and Thermal Mask Images

In this paper, we study performance and fairness on visual and thermal images and expand the assessment to masked synthetic images. Using the SpeakingFace and Thermal-Mask dataset, we propose a process to assess fairness on real images and show how the same process can be applied to synthetic images. The resulting process shows a demographic parity difference of 1.59 for random guessing and increases to 5.0 when the recognition performance increases to a precision and recall rate of 99.99\%. We indicate that inherently biased datasets can deeply impact the fairness of any biometric system. A primary cause of a biased dataset is the class imbalance due to the data collection process. To address imbalanced datasets, the classes with fewer samples can be augmented with synthetic images to generate a more balanced dataset resulting in less bias when training a machine learning system. For biometric-enabled systems, fairness is of critical importance, while the related concept of Equity, Diversity, and Inclusion (EDI) is well suited for the generalization of fairness in biometrics, in this paper, we focus on the 3 most common demographic groups age, gender, and ethnicity.Comment: 6 pages, 3 figure

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

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.Comment: submitted to IEEE International Conference on Systems, Man, and Cybernetic

Cognitive Checkpoint: Emerging Technologies for Biometric-Enabled Watchlist Screening

This paper revisits the problem of individual risk assessment in the layered security model. It contributes to the concept of balancing security and privacy via cognitive-centric machine called an ’e-interviewer’. Cognitive checkpoint is a cyber-physical security frontier in mass-transit hubs that provides an automated screening using all types of identity (attributed, biometric, and biographical) from both physical and virtual worlds. We investigate how the development of the next generation of watchlist for rapid screening impacts a sensitive balancing mechanism between security and privacy. We identify directions of such an impact, trends in watchlist technologies, and propose ways to mitigate the potential risks

On the number of generators for transeunt triangles

This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. As such, it is in the public domain, and under the provisions of Title 17, United States Code, Section 105, may not be copyrighted.Discrete Applied Mathematics, 108, 2001, pp. 309-316A transeunt triangle for size n consists of (n+1)x(n+1)x(n+1) 0's and 1's whose values are determined by the sum modulo 2 of two other local values. For a given n, two transeunt triangles of size n can be combined using the element-by-element modulo 2 sum to generate a third transeunt triangle. We show that, for large n ..

Cognitive Identity Management: Synthetic Data, Risk and Trust

Synthetic, or artificial data is used in security applications such as protection of sensitive information, prediction of rare events, and training neural networks. Risk and trust are assessed specifically for a given kind of synthetic data and particular application. In this paper, we consider a more complicated scenario, – biometric-enabled cognitive cognitive biometric-enabled identity management, in which multiple kinds of synthetic data are used in addition to authentic data. For example, authentic biometric traits can be used to train the intelligent tools to identify humans, while synthetic, algorithmically generated data can be used to expand the training set or to model extreme situations. This paper is dedicated to understanding the potential impact of synthetic data on the cognitive checkpoint performance, and risk and trust prediction

Assessing Risks of Biases in Cognitive Decision Support Systems

Recognizing, assessing, countering, and mitigating the biases of different nature from heterogeneous sources is a critical problem in designing a cognitive Decision Support System (DSS). An example of such a system is a cognitive biometric-enabled security checkpoint. Biased algorithms affect the decision-making process in an unpredictable way, e.g. face recognition for different demographic groups may severely impact the risk assessment at a checkpoint. This paper addresses a challenging research question on how to manage an ensemble of biases? We provide performance projections of the DSS operational landscape in terms of biases. A probabilistic reasoning technique is used for assessment of the risk of such biases. We also provide a motivational experiment using face biometric component of the checkpoint system which highlights the discovery of an ensemble of biases and the techniques to assess their risks.Comment: submitted to 28th European Signal Processing Conference (EUSIPCO 2020

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

This paper reports the Technology Gap (TG) navigator, a novel tool for individual risk assessment in the layered security infrastructure. It is motivated by the practical need of the biometricenabled security systems design. The tool helps specify the conditions for bridging the identified TGs. The input data for the TG navigator includes 1) a causal description of the TG, 2) statistics regarding the available resources and performances, and 3) the required performance. The output includes generated probabilistic conditions, and the corresponding technology requirements for bridging the targeted TG