115 citations · 369 across the 19 of their papers we have counts for
25 papers
Bias Mitigation in Face Recognition via Demographic-based Supervised Contrastive Learning
Yu Linghu, Salman Mohammad, Xinyi Zhang +1
Face recognition systems have been shown to be biased toward certain demographic groups by exhibiting different error rates across gender, age, or ethnicity. Though the imbalance o…
Detecting Drunk Driving Using Off-the-Shelf Smartwatches
Robin Deuber, Lanlan Yang, Michal Bechny +9
Alcohol-impaired driving remains a major yet preventable cause of road traffic injury and death, with many drivers underestimating their level of intoxication. Compared to in-vehic…
FAME: Feature Activation Map Explanation on Image Classification and Face Recognition
Xinyi Zhang, Manuel Günther
Deep Learning has revolutionized machine learning, reaching unprecedented levels of accuracy, but at the cost of reduced interpretability. Especially in image processing systems, d…
GHOST: Gaussian Hypothesis Open-Set Technique
Ryan Rabinowitz, Steve Cruz, Manuel Günther +1
Evaluations of large-scale recognition methods typically focus on overall performance. While this approach is common, it often fails to provide insights into performance across ind…
Score Normalization for Demographic Fairness in Face Recognition
Yu Linghu, Tiago de Freitas Pereira, Christophe Ecabert +2
Fair biometric algorithms have similar verification performance across different demographic groups given a single decision threshold. Unfortunately, for state-of-the-art face reco…
Refining Tuberculosis Detection in CXR Imaging: Addressing Bias in Deep Neural Networks via Interpretability
Özgür Acar Güler, Manuel Günther, André Anjos
Automatic classification of active tuberculosis from chest X-ray images has the potential to save lives, especially in low- and mid-income countries where skilled human experts can…
Large-Scale Evaluation of Open-Set Image Classification Techniques
Halil Bisgin, Andres Palechor, Mike Suter +1
The goal for classification is to correctly assign labels to unseen samples. However, most methods misclassify samples with unseen labels and assign them to one of the known classe…
Biased Binary Attribute Classifiers Ignore the Majority Classes
Xinyi Zhang, Johanna Sophie Bieri, Manuel Günther
To visualize the regions of interest that classifiers base their decisions on, different Class Activation Mapping (CAM) methods have been developed. However, all of these technique…
Bridging Trustworthiness and Open-World Learning: An Exploratory Neural Approach for Enhancing Interpretability, Generalization, and Robustness
Shide Du, Zihan Fang, Shiyang Lan +4
As researchers strive to narrow the gap between machine intelligence and human through the development of artificial intelligence technologies, it is imperative that we recognize t…
Large-Scale Open-Set Classification Protocols for ImageNet
Andres Palechor, Annesha Bhoumik, Manuel Günther
Open-Set Classification (OSC) intends to adapt closed-set classification models to real-world scenarios, where the classifier must correctly label samples of known classes while re…
Consistency and Accuracy of CelebA Attribute Values
Haiyu Wu, Grace Bezold, Manuel Günther +3
We report the first systematic analysis of the experimental foundations of facial attribute classification. Two annotators independently assigning attribute values shows that only…
Eight Years of Face Recognition Research: Reproducibility, Achievements and Open Issues
Tiago de Freitas Pereira, Dominic Schmidli, Yu Linghu +3
Automatic face recognition is a research area with high popularity. Many different face recognition algorithms have been proposed in the last thirty years of intensive research in…
Reducing Network Agnostophobia
Akshay Raj Dhamija, Manuel Günther, Terrance E. Boult
Agnostophobia, the fear of the unknown, can be experienced by deep learning engineers while applying their networks to real-world applications. Unfortunately, network behavior is n…
Facial Attributes: Accuracy and Adversarial Robustness
Andras Rozsa, Manuel Günther, Ethan M. Rudd +1
Facial attributes, emerging soft biometrics, must be automatically and reliably extracted from images in order to be usable in stand-alone systems. While recent methods extract fac…
Adversarial Robustness: Softmax versus Openmax
Andras Rozsa, Manuel Günther, Terrance E. Boult
Deep neural networks (DNNs) provide state-of-the-art results on various tasks and are widely used in real world applications. However, it was discovered that machine learning model…
Unconstrained Face Detection and Open-Set Face Recognition Challenge
Manuel Günther, Peiyun Hu, Christian Herrmann +12
Face detection and recognition benchmarks have shifted toward more difficult environments. The challenge presented in this paper addresses the next step in the direction of automat…
Toward Open-Set Face Recognition
Manuel Günther, Steve Cruz, Ethan M. Rudd +1
Much research has been conducted on both face identification and face verification, with greater focus on the latter. Research on face identification has mostly focused on using cl…
Towards Robust Deep Neural Networks with BANG
Andras Rozsa, Manuel Gunther, Terrance E. Boult
Machine learning models, including state-of-the-art deep neural networks, are vulnerable to small perturbations that cause unexpected classification errors. This unexpected lack of…
Are Accuracy and Robustness Correlated?
Andras Rozsa, Manuel Günther, Terrance E. Boult
Machine learning models are vulnerable to adversarial examples formed by applying small carefully chosen perturbations to inputs that cause unexpected classification errors. In thi…
LOTS about Attacking Deep Features
Andras Rozsa, Manuel Günther, Terrance E. Boult
Deep neural networks provide state-of-the-art performance on various tasks and are, therefore, widely used in real world applications. DNNs are becoming frequently utilized in biom…
AFFACT - Alignment-Free Facial Attribute Classification Technique
Manuel Günther, Andras Rozsa, Terrance E. Boult
Facial attributes are soft-biometrics that allow limiting the search space, e.g., by rejecting identities with non-matching facial characteristics such as nose sizes or eyebrow sha…
PARAPH: Presentation Attack Rejection by Analyzing Polarization Hypotheses
Ethan M. Rudd, Manuel Gunther, Terrance E. Boult
For applications such as airport border control, biometric technologies that can process many capture subjects quickly, efficiently, with weak supervision, and with minimal discomf…
Are Facial Attributes Adversarially Robust?
Andras Rozsa, Manuel Günther, Ethan M. Rudd +1
Facial attributes are emerging soft biometrics that have the potential to reject non-matches, for example, based on mismatching gender. To be usable in stand-alone systems, facial…
MOON: A Mixed Objective Optimization Network for the Recognition of Facial Attributes
Ethan Rudd, Manuel Günther, Terrance Boult
Attribute recognition, particularly facial, extracts many labels for each image. While some multi-task vision problems can be decomposed into separate tasks and stages, e.g., train…