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Found 14 papers, 4 papers with code
The Casual Conversations v2 Dataset
This paper introduces a new large consent-driven dataset aimed at assisting in the evaluation of algorithmic bias and robustness of computer vision and audio speech models in regards to 11 attributes that are self-provided or labeled by trained annotators.
You Only Need a Good Embeddings Extractor to Fix Spurious Correlations
With experiments on a wide range of pre-trained models and pre-training datasets, we show that the capacity of the pre-training model and the size of the pre-training dataset matters.
Casual Conversations v2: Designing a large consent-driven dataset to measure algorithmic bias and robustness
Developing robust and fair AI systems require datasets with comprehensive set of labels that can help ensure the validity and legitimacy of relevant measurements.
The Gender Gap in Face Recognition Accuracy Is a Hairy Problem
We then demonstrate that when the data used to estimate recognition accuracy is balanced across gender for how hairstyles occlude the face, the initially observed gender gap in accuracy largely disappears.
Face Recognition Accuracy Across Demographics: Shining a Light Into the Problem
This is the first work that we are aware of to explore how the level of brightness of the skin region in a pair of face images (rather than a single image) impacts face recognition accuracy, and to evaluate this as a systematic factor causing unequal accuracy across demographics.
Gendered Differences in Face Recognition Accuracy Explained by Hairstyles, Makeup, and Facial Morphology
There is consensus in the research literature that face recognition accuracy is lower for females, who often have both a higher false match rate and a higher false non-match rate.
Does Face Recognition Error Echo Gender Classification Error?
For impostor image pairs, our results show that pairs in which one image has a gender classification error have a better impostor distribution than pairs in which both images have correct gender classification, and so are less likely to generate a false match error.
img2pose: Face Alignment and Detection via 6DoF, Face Pose Estimation
Tests on AFLW2000-3D and BIWI show that our method runs at real-time and outperforms state of the art (SotA) face pose estimators.
Ranked #6 on
Head Pose Estimation
on BIWI
Is Face Recognition Sexist? No, Gendered Hairstyles and Biology Are
There is consensus in the research literature that face recognition accuracy is lower for females, who often have both a higher false match rate and a higher false non-match rate.
A Method for Curation of Web-Scraped Face Image Datasets
The numbers of subjects and images acquired in web-scraped datasets are usually very large, with number of images on the millions scale.
How Does Gender Balance In Training Data Affect Face Recognition Accuracy?
Deep learning methods have greatly increased the accuracy of face recognition, but an old problem still persists: accuracy is usually higher for men than women.
Analysis of Gender Inequality In Face Recognition Accuracy
We show that the female genuine distribution improves when only female images without facial cosmetics are used, but that the female impostor distribution also degrades at the same time.
Identity Document to Selfie Face Matching Across Adolescence
Matching live images (``selfies'') to images from ID documents is a problem that can arise in various applications.
Does Face Recognition Accuracy Get Better With Age? Deep Face Matchers Say No
In contrast, a pre deep learning matcher on the same dataset shows the traditional result of higher accuracy for older persons, although its overall accuracy is much lower than that of the deep learning matchers.
