33.1k citations · 33.4k across the 52 of their papers we have counts for
65 papers
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…
State Of The Art In Open-Set Iris Presentation Attack Detection
Aidan Boyd, Jeremy Speth, Lucas Parzianello +2
Research in presentation attack detection (PAD) for iris recognition has largely moved beyond evaluation in "closed-set" scenarios, to emphasize ability to generalize to presentati…
The Value of AI Guidance in Human Examination of Synthetically-Generated Faces
Aidan Boyd, Patrick Tinsley, Kevin Bowyer +1
Face image synthesis has progressed beyond the point at which humans can effectively distinguish authentic faces from synthetically generated ones. Recently developed synthetic fac…
Analyzing the Impact of Shape & Context on the Face Recognition Performance of Deep Networks
Sandipan Banerjee, Walter Scheirer, Kevin Bowyer +1
In this article, we analyze how changing the underlying 3D shape of the base identity in face images can distort their overall appearance, especially from the perspective of deep f…
Human Saliency-Driven Patch-based Matching for Interpretable Post-mortem Iris Recognition
Aidan Boyd, Daniel Moreira, Andrey Kuehlkamp +2
Forensic iris recognition, as opposed to live iris recognition, is an emerging research area that leverages the discriminative power of iris biometrics to aid human examiners in th…
The Gender Gap in Face Recognition Accuracy Is a Hairy Problem
Aman Bhatta, Vítor Albiero, Kevin W. Bowyer +1
It is broadly accepted that there is a "gender gap" in face recognition accuracy, with females having higher false match and false non-match rates. However, relatively little is kn…
Face Recognition Accuracy Across Demographics: Shining a Light Into the Problem
Haiyu Wu, Vítor Albiero, K. S. Krishnapriya +2
We explore varying face recognition accuracy across demographic groups as a phenomenon partly caused by differences in face illumination. We observe that for a common operational s…
Gendered Differences in Face Recognition Accuracy Explained by Hairstyles, Makeup, and Facial Morphology
Vítor Albiero, Kai Zhang, Michael C. King +1
Media reports have accused face recognition of being ''biased'', ''sexist'' and ''racist''. There is consensus in the research literature that face recognition accuracy is lower fo…
Interpretable Deep Learning-Based Forensic Iris Segmentation and Recognition
Andrey Kuehlkamp, Aidan Boyd, Adam Czajka +4
Iris recognition of living individuals is a mature biometric modality that has been adopted globally from governmental ID programs, border crossing, voter registration and de-dupli…
CYBORG: Blending Human Saliency Into the Loss Improves Deep Learning
Aidan Boyd, Patrick Tinsley, Kevin Bowyer +1
Can deep learning models achieve greater generalization if their training is guided by reference to human perceptual abilities? And how can we implement this in a practical manner?…
A Study of the Human Perception of Synthetic Faces
Bingyu Shen, Brandon RichardWebster, Alice O'Toole +2
Advances in face synthesis have raised alarms about the deceptive use of synthetic faces. Can synthetic identities be effectively used to fool human observers? In this paper, we in…
Digital and Physical-World Attacks on Remote Pulse Detection
Jeremy Speth, Nathan Vance, Patrick Flynn +2
Remote photoplethysmography (rPPG) is a technique for estimating blood volume changes from reflected light without the need for a contact sensor. We present the first examples of p…
Deception Detection and Remote Physiological Monitoring: A Dataset and Baseline Experimental Results
Jeremy Speth, Nathan Vance, Adam Czajka +3
We present the Deception Detection and Physiological Monitoring (DDPM) dataset and initial baseline results on this dataset. Our application context is an interview scenario in whi…
Human-Aided Saliency Maps Improve Generalization of Deep Learning
Aidan Boyd, Kevin Bowyer, Adam Czajka
Deep learning has driven remarkable accuracy increases in many computer vision problems. One ongoing challenge is how to achieve the greatest accuracy in cases where training data…
Analysis of Manual and Automated Skin Tone Assignments for Face Recognition Applications
KS Krishnapriya, Michael C. King, Kevin W. Bowyer
News reports have suggested that darker skin tone causes an increase in face recognition errors. The Fitzpatrick scale is widely used in dermatology to classify sensitivity to sun…