33.1k citations · 33.4k across the 52 of their papers we have counts for
10 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…
LogicNet: A Logical Consistency Embedded Face Attribute Learning Network
Haiyu Wu, Sicong Tian, Huayu Li +1
Ensuring logical consistency in predictions is a crucial yet overlooked aspect in multi-attribute classification. We explore the potential reasons for this oversight and introduce…
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…
Teaching AI to Teach: Leveraging Limited Human Salience Data Into Unlimited Saliency-Based Training
Colton R. Crum, Aidan Boyd, Kevin Bowyer +1
Machine learning models have shown increased accuracy in classification tasks when the training process incorporates human perceptual information. However, a challenge in training…
Exploring Causes of Demographic Variations In Face Recognition Accuracy
Gabriella Pangelinan, K. S. Krishnapriya, Vitor Albiero +5
In recent years, media reports have called out bias and racism in face recognition technology. We review experimental results exploring several speculated causes for asymmetric cro…
What Should Be Balanced in a "Balanced" Face Recognition Dataset?
Haiyu Wu, Kevin W. Bowyer
The issue of demographic disparities in face recognition accuracy has attracted increasing attention in recent years. Various face image datasets have been proposed as 'fair' or 'b…
Explain To Me: Salience-Based Explainability for Synthetic Face Detection Models
Colton Crum, Patrick Tinsley, Aidan Boyd +5
The performance of convolutional neural networks has continued to improve over the last decade. At the same time, as model complexity grows, it becomes increasingly more difficult…
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…