4,919 research outputs found
Bias in Deep Learning and Applications to Face Analysis
Deep learning has fostered the progress in the field of face analysis, resulting in the integration of these models in multiple aspects of society. Even though the majority of research has focused on optimizing standard evaluation metrics, recent work has exposed the bias of such algorithms as well as the dangers of their unaccountable utilization.n this thesis, we explore the bias of deep learning models in the discriminative and the generative setting. We begin by investigating the bias of face analysis models with regards to different demographics. To this end, we collect KANFace, a large-scale video and image dataset of faces captured ``in-the-wild’'. The rich set of annotations allows us to expose the demographic bias of deep learning models, which we mitigate by utilizing adversarial learning to debias the deep representations. Furthermore, we explore neural augmentation as a strategy towards training fair classifiers. We propose a style-based multi-attribute transfer framework that is able to synthesize photo-realistic faces of the underrepresented demographics. This is achieved by introducing a multi-attribute extension to Adaptive Instance Normalisation that captures the multiplicative interactions between the representations of different attributes. Focusing on bias in gender recognition, we showcase the efficacy of the framework in training classifiers that are more fair compared to generative and fairness-aware methods.In the second part, we focus on bias in deep generative models. In particular, we start by studying the generalization of generative models on images of unseen attribute combinations. To this end, we extend the conditional Variational Autoencoder by introducing a multilinear conditioning framework. The proposed method is able to synthesize unseen attribute combinations by modeling the multiplicative interactions between the attributes. Lastly, in order to control protected attributes, we investigate controlled image generation without training on a labelled dataset. We leverage pre-trained Generative Adversarial Networks that are trained in an unsupervised fashion and exploit the clustering that occurs in the representation space of intermediate layers of the generator. We show that these clusters capture semantic attribute information and condition image synthesis on the cluster assignment using Implicit Maximum Likelihood Estimation.Open Acces
Face Image Quality Assessment: A Literature Survey
The performance of face analysis and recognition systems depends on the
quality of the acquired face data, which is influenced by numerous factors.
Automatically assessing the quality of face data in terms of biometric utility
can thus be useful to detect low-quality data and make decisions accordingly.
This survey provides an overview of the face image quality assessment
literature, which predominantly focuses on visible wavelength face image input.
A trend towards deep learning based methods is observed, including notable
conceptual differences among the recent approaches, such as the integration of
quality assessment into face recognition models. Besides image selection, face
image quality assessment can also be used in a variety of other application
scenarios, which are discussed herein. Open issues and challenges are pointed
out, i.a. highlighting the importance of comparability for algorithm
evaluations, and the challenge for future work to create deep learning
approaches that are interpretable in addition to providing accurate utility
predictions
A Review of Deep Learning Methods for Photoplethysmography Data
Photoplethysmography (PPG) is a highly promising device due to its advantages
in portability, user-friendly operation, and non-invasive capabilities to
measure a wide range of physiological information. Recent advancements in deep
learning have demonstrated remarkable outcomes by leveraging PPG signals for
tasks related to personal health management and other multifaceted
applications. In this review, we systematically reviewed papers that applied
deep learning models to process PPG data between January 1st of 2017 and July
31st of 2023 from Google Scholar, PubMed and Dimensions. Each paper is analyzed
from three key perspectives: tasks, models, and data. We finally extracted 193
papers where different deep learning frameworks were used to process PPG
signals. Based on the tasks addressed in these papers, we categorized them into
two major groups: medical-related, and non-medical-related. The medical-related
tasks were further divided into seven subgroups, including blood pressure
analysis, cardiovascular monitoring and diagnosis, sleep health, mental health,
respiratory monitoring and analysis, blood glucose analysis, as well as others.
The non-medical-related tasks were divided into four subgroups, which encompass
signal processing, biometric identification, electrocardiogram reconstruction,
and human activity recognition. In conclusion, significant progress has been
made in the field of using deep learning methods to process PPG data recently.
This allows for a more thorough exploration and utilization of the information
contained in PPG signals. However, challenges remain, such as limited quantity
and quality of publicly available databases, a lack of effective validation in
real-world scenarios, and concerns about the interpretability, scalability, and
complexity of deep learning models. Moreover, there are still emerging research
areas that require further investigation
Engineering data compendium. Human perception and performance. User's guide
The concept underlying the Engineering Data Compendium was the product of a research and development program (Integrated Perceptual Information for Designers project) aimed at facilitating the application of basic research findings in human performance to the design and military crew systems. The principal objective was to develop a workable strategy for: (1) identifying and distilling information of potential value to system design from the existing research literature, and (2) presenting this technical information in a way that would aid its accessibility, interpretability, and applicability by systems designers. The present four volumes of the Engineering Data Compendium represent the first implementation of this strategy. This is the first volume, the User's Guide, containing a description of the program and instructions for its use
Millimetre waves, lasers, acoustics for non-lethal weapons? Physics analyses and inferences
"New technologies of non-lethal weapons (NLW) are under military research and
development, mainly in the USA. Due to incomplete information, judgement under criteria
of the laws of warfare or of human rights is hampered. This study analyses four potential
NLW technologies which are based on physics to provide reliable information for such
assessment." (excerpt)"Neue Techniken nicht-tödlicher Waffen (ntW) sind in militärischer Forschung und
Entwicklung, vor allem in den USA. Durch unvollständige Information wird die Beurteilung
unter Kriterien des Humanitären Völkerrechts oder der Menschenrechte behindert. Diese
Studie analysiert vier mögliche ntW-Techniken, die auf physikalischer Grundlage beruhen,
um verlässliche Information für eine solche Beurteilung zur Verfügung zu stellen." (Textauszug
Learning Domain Invariant Information to Enhance Presentation Attack Detection in Visible Face Recognition Systems
Face signatures, including size, shape, texture, skin tone, eye color, appearance, and scars/marks, are widely used as discriminative, biometric information for access control. Despite recent advancements in facial recognition systems, presentation attacks on facial recognition systems have become increasingly sophisticated. The ability to detect presentation attacks or spoofing attempts is a pressing concern for the integrity, security, and trust of facial recognition systems. Multi-spectral imaging has been previously introduced as a way to improve presentation attack detection by utilizing sensors that are sensitive to different regions of the electromagnetic spectrum (e.g., visible, near infrared, long-wave infrared). Although multi-spectral presentation attack detection systems may be discriminative, the need for additional sensors and computational resources substantially increases complexity and costs. Instead, we propose a method that exploits information from infrared imagery during training to increase the discriminability of visible-based presentation attack detection systems. We introduce (1) a new cross-domain presentation attack detection framework that increases the separability of bonafide and presentation attacks using only visible spectrum imagery, (2) an inverse domain regularization technique for added training stability when optimizing our cross-domain presentation attack detection framework, and (3) a dense domain adaptation subnetwork to transform representations between visible and non-visible domains.
Adviser: Benjamin Rigga
Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 141)
This special bibliography lists 267 reports, articles, and other documents introduced into the NASA scientific and technical information system in April 1975
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