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Found 13 papers, 3 papers with code
What is a Goldilocks Face Verification Test Set?
We show that current train and test sets are generally not identity- or even image-disjoint, and that this results in an optimistic bias in the estimated accuracy.
Impact of Blur and Resolution on Demographic Disparities in 1-to-Many Facial Identification
This paper analyzes the accuracy of 1-to-many facial identification across demographic groups, and in the presence of blur and reduced resolution in the probe image as might occur in "surveillance camera quality" images.
Analysis of Adversarial Image Manipulations
As virtual and physical identity grow increasingly intertwined, the importance of privacy and security in the online sphere becomes paramount.
Exploring Causes of Demographic Variations In Face Recognition Accuracy
In recent years, media reports have called out bias and racism in face recognition technology.
Consistency and Accuracy of CelebA Attribute Values
Two annotators independently assigning attribute values shows that only 12 of 40 common attributes are assigned values with >= 95% consistency, and three (high cheekbones, pointed nose, oval face) have essentially random consistency.
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.
Analysis of Manual and Automated Skin Tone Assignments for Face Recognition Applications
News reports have suggested that darker skin tone causes an increase in face recognition errors.
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.
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.
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.
Characterizing the Variability in Face Recognition Accuracy Relative to Race
For a fixed decision threshold, the African-American image cohort has a higher false match rate and a lower false non-match rate.
