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Found 28 papers, 2 papers with code
Equal Confusion Fairness: Measuring Group-Based Disparities in Automated Decision Systems
Generalizing this intuition, this paper proposes a new equal confusion fairness test to check an automated decision system for fairness and a new confusion parity error to quantify the extent of any unfairness.
Accuracy, Fairness, and Interpretability of Machine Learning Criminal Recidivism Models
Criminal recidivism models are tools that have gained widespread adoption by parole boards across the United States to assist with parole decisions.
Error Parity Fairness: Testing for Group Fairness in Regression Tasks
Overall, the proposed regression fairness testing methodology fills a gap in the fair machine learning literature and may serve as a part of larger accountability assessments and algorithm audits.
System Cards for AI-Based Decision-Making for Public Policy
This work also proposes system cards to serve as scorecards presenting the outcomes of such audits.
A Case Study of Deep Learning Based Multi-Modal Methods for Predicting the Age-Suitability Rating of Movie Trailers
In this work, we explore different approaches to combine modalities for the problem of automated age-suitability rating of movie trailers.
DBLFace: Domain-Based Labels for NIR-VIS Heterogeneous Face Recognition
In this paper, we introduce Domain-Based Label Face (DBLFace), a learning approach based on the assumption that a subject is not represented by a single label but by a set of labels.
DETCID: Detection of Elongated Touching Cells with Inhomogeneous Illumination using a Deep Adversarial Network
However, detecting C. diff cells in SEM images is a challenging problem due to the presence of inhomogeneous illumination and occlusion.
On Improving the Generalization of Face Recognition in the Presence of Occlusions
In this paper, we address a key limitation of existing 2D face recognition methods: robustness to occlusions.
Adversarial Representation Learning for Text-to-Image Matching
For many computer vision applications such as image captioning, visual question answering, and person search, learning discriminative feature representations at both image and text level is an essential yet challenging problem.
Occlusion-guided compact template learning for ensemble deep network-based pose-invariant face recognition
The compact face representation is not sensitive to the number of patches that are used to construct the facial template and is more suitable for incorporating the information from different view angles for image-set based face recognition.
Open Source Face Recognition Performance Evaluation Package
Biometrics-related research has been accelerated significantly by deep learning technology.
Deep Imbalanced Attribute Classification using Visual Attention Aggregation
For many computer vision applications, such as image description and human identification, recognizing the visual attributes of humans is an essential yet challenging problem.
Fully Associative Patch-based 1-to-N Matcher for Face Recognition
A Fully Associative Patch-based Signature Matcher (FAPSM) is proposed so that the local matching identity of each patch contributes to the global matching identities of all the patches.
A Hierarchical Matcher using Local Classifier Chains
This paper focuses on improving the performance of current convolutional neural networks in visual recognition without changing the network architecture.
Patch-based Face Recognition using a Hierarchical Multi-label Matcher
Three ways are introduced to learn the global matching: majority voting, l1-regularized weighting, and decision rule.
A Face Recognition Signature Combining Patch-based Features with Soft Facial Attributes
This paper focuses on improving face recognition performance with a new signature combining implicit facial features with explicit soft facial attributes.
Convolutional Point-set Representation: A Convolutional Bridge Between a Densely Annotated Image and 3D Face Alignment
The method relies on Convolutional Point-set Representation (CPR), a carefully designed matrix representation to summarize different layers of information encoded in the set of detected points in the annotated image.
Curriculum Learning of Visual Attribute Clusters for Multi-Task Classification
In this paper, we introduce a novel method to combine the advantages of both multi-task and curriculum learning in a visual attribute classification framework.
Human Activity Recognition Using Robust Adaptive Privileged Probabilistic Learning
In this work, a novel method based on the learning using privileged information (LUPI) paradigm for recognizing complex human activities is proposed that handles missing information during testing.
When 3D-Aided 2D Face Recognition Meets Deep Learning: An extended UR2D for Pose-Invariant Face Recognition
Extensive experiments are conducted on the UHDB31 and IJB-A, demonstrating that UR2D outperforms existing 2D face recognition systems such as VGG-Face, FaceNet, and a commercial off-the-shelf software (COTS) by at least 9% on the UHDB31 dataset and 3% on the IJB-A dataset on average in face identification tasks.
Facial 3D Model Registration Under Occlusions With SensiblePoints-based Reinforced Hypothesis Refinement
The visual clues extracted from the fiducial points, non-fiducial points, and facial contour are jointly employed to verify the hypotheses.
Inferring Human Activities Using Robust Privileged Probabilistic Learning
Classification models may often suffer from "structure imbalance" between training and testing data that may occur due to the deficient data collection process.
Adaptive SVM+: Learning with Privileged Information for Domain Adaptation
Incorporating additional knowledge in the learning process can be beneficial for several computer vision and machine learning tasks.
Curriculum Learning for Multi-Task Classification of Visual Attributes
Visual attributes, from simple objects (e. g., backpacks, hats) to soft-biometrics (e. g., gender, height, clothing) have proven to be a powerful representational approach for many applications such as image description and human identification.
End-to-end 3D face reconstruction with deep neural networks
Inspired by the success of deep neural networks (DNN), we propose a DNN-based approach for End-to-End 3D FAce Reconstruction (UH-E2FAR) from a single 2D image.
GoDP: Globally optimized dual pathway system for facial landmark localization in-the-wild
Facial landmark localization is a fundamental module for pose-invariant face recognition.
Predicting Privileged Information for Height Estimation
In this paper, we propose a novel regression-based method for employing privileged information to estimate the height using human metrology.
PDM-ENLOR: Learning Ensemble of Local PDM-Based Regressions
For each model point, an ensemble of regressors is built.
