2 citations · 2 across the 12 of their papers we have counts for
5 papers · 2 filters
CRAFT: Contextual Re-Activation of Filters for face recognition Training
Aman Bhatta, Domingo Mery, Haiyu Wu +1
The first layer of a deep CNN backbone applies filters to an image to extract the basic features available to later layers. During training, some filters may go inactive, mean ing…
What's color got to do with it? Face recognition in grayscale
Aman Bhatta, Domingo Mery, Haiyu Wu +3
State-of-the-art deep CNN face matchers are typically created using extensive training sets of color face images. Our study reveals that such matchers attain virtually identical ac…
Impact of Blur and Resolution on Demographic Disparities in 1-to-Many Facial Identification
Aman Bhatta, Gabriella Pangelinan, Michael C. King +1
Most studies to date that have examined demographic variations in face recognition accuracy have analyzed 1-to-1 matching accuracy, using images that could be described as "governm…
Beard Segmentation and Recognition Bias
Kagan Ozturk, Grace Bezold, Aman Bhatta +2
A person's facial hairstyle, such as presence and size of beard, can significantly impact face recognition accuracy. There are publicly-available deep networks that achieve reasona…
Logical Consistency and Greater Descriptive Power for Facial Hair Attribute Learning
Haiyu Wu, Grace Bezold, Aman Bhatta +1
Face attribute research has so far used only simple binary attributes for facial hair; e.g., beard / no beard. We have created a new, more descriptive facial hair annotation scheme…