12 citations · 32 across the 6 of their papers we have counts for
16 papers · 1 filter
The Influence of the Other-Race Effect on Susceptibility to Face Morphing Attacks
Snipta Mallick, Geraldine Jeckeln, Connor J. Parde +2
Facial morphs created between two identities resemble both of the faces used to create the morph. Consequently, humans and machines are prone to mistake morphs made from two identi…
PASS: Protected Attribute Suppression System for Mitigating Bias in Face Recognition
Prithviraj Dhar, Joshua Gleason, Aniket Roy +2
Face recognition networks encode information about sensitive attributes while being trained for identity classification. Such encoding has two major issues: (a) it makes the face r…
Towards Gender-Neutral Face Descriptors for Mitigating Bias in Face Recognition
Prithviraj Dhar, Joshua Gleason, Hossein Souri +2
State-of-the-art deep networks implicitly encode gender information while being trained for face recognition. Gender is often viewed as an important attribute with respect to ident…
Single Unit Status in Deep Convolutional Neural Network Codes for Face Identification: Sparseness Redefined
Connor J. Parde, Y. Ivette Colón, Matthew Q. Hill +3
Deep convolutional neural networks (DCNNs) trained for face identification develop representations that generalize over variable images, while retaining subject (e.g., gender) and…
Accuracy comparison across face recognition algorithms: Where are we on measuring race bias?
Jacqueline G. Cavazos, P. Jonathon Phillips, Carlos D. Castillo +1
Previous generations of face recognition algorithms differ in accuracy for images of different races (race bias). Here, we present the possible underlying factors (data-driven and…
How are attributes expressed in face DCNNs?
Prithviraj Dhar, Ankan Bansal, Carlos D. Castillo +3
As deep networks become increasingly accurate at recognizing faces, it is vital to understand how these networks process faces. While these networks are solely trained to recognize…