REVIEW 5 cited by
Diversity in Faces
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Face recognition is a long standing challenge in the field of Artificial Intelligence (AI). The goal is to create systems that accurately detect, recognize, verify, and understand human faces. There are significant technical hurdles in making these systems accurate, particularly in unconstrained settings due to confounding factors related to pose, resolution, illumination, occlusion, and viewpoint. However, with recent advances in neural networks, face recognition has achieved unprecedented accuracy, largely built on data-driven deep learning methods. While this is encouraging, a critical aspect that is limiting facial recognition accuracy and fairness is inherent facial diversity. Every face is different. Every face reflects something unique about us. Aspects of our heritage - including race, ethnicity, culture, geography - and our individual identify - age, gender, and other visible manifestations of self-expression, are reflected in our faces. We expect face recognition to work equally accurately for every face. Face recognition needs to be fair. As we rely on data-driven methods to create face recognition technology, we need to ensure necessary balance and coverage in training data. However, there are still scientific questions about how to represent and extract pertinent facial features and quantitatively measure facial diversity. Towards this goal, Diversity in Faces (DiF) provides a data set of one million annotated human face images for advancing the study of facial diversity. The annotations are generated using ten well-established facial coding schemes from the scientific literature. The facial coding schemes provide human-interpretable quantitative measures of facial features. We believe that by making the extracted coding schemes available on a large set of faces, we can accelerate research and development towards creating more fair and accurate facial recognition systems.
Forward citations
Cited by 5 Pith papers
-
Face4FairShifts: A Large Image Benchmark for Fairness and Robust Learning across Visual Domains
A new face benchmark with four visual domains and fairness-sensitive labels provides larger measured distribution shifts and lower baseline performance than existing fairness datasets.
-
Fairness-Aware Grouping for Continuous Sensitive Variables: Application for Debiasing Face Analysis with respect to Skin Tone
A fairness-based grouping algorithm that partitions a continuous sensitive attribute into subgroups with maximally different discrimination levels, validated on synthetic data and face images.
-
Underage Detection through a Multi-Task and MultiAge Approach for Screening Minors in Unconstrained Imagery
A multi-task face model with four underage thresholds and a frozen FaRL backbone, trained with age-balanced resampling and an age gap, improves underage detection on new benchmarks ASORES-39k and ASWIFT-20k.
-
Steps Adaptive Decay DPSGD: Enhancing Performance on Imbalanced Datasets with Differential Privacy with HAM10000
SAD-DPSGD, a step-adaptive decay schedule for noise and clipping in DP-SGD, reports about 1% higher accuracy than Auto-DPSGD on HAM10000 under differential privacy.
-
Object Recognition Datasets and Challenges: A Review
A review paper that compiles statistics and descriptions of over 160 object recognition datasets, their associated challenges, and evaluation metrics.
Discussion (0). Continue with ORCID to comment.