25 research outputs found
Multi-Metric Evaluation of Thermal-to-Visual Face Recognition
In this paper, we aim to address the problem of heterogeneous or
cross-spectral face recognition using machine learning to synthesize visual
spectrum face from infrared images. The synthesis of visual-band face images
allows for more optimal extraction of facial features to be used for face
identification and/or verification. We explore the ability to use Generative
Adversarial Networks (GANs) for face image synthesis, and examine the
performance of these images using pre-trained Convolutional Neural Networks
(CNNs). The features extracted using CNNs are applied in face identification
and verification. We explore the performance in terms of acceptance rate when
using various similarity measures for face verification
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
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