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Found 13 papers, 0 papers with code
Gaussian Harmony: Attaining Fairness in Diffusion-based Face Generation Models
We mitigate the bias by localizing the means of the facial attributes in the latent space of the diffusion model using Gaussian mixture models (GMM).
Recognizing People by Body Shape Using Deep Networks of Images and Words
Although the non-linguistic model yielded fewer false accepts at all distances, fusion of the linguistic and non-linguistic models decreased false accepts for all, but the UAV images.
Human-Machine Comparison for Cross-Race Face Verification: Race Bias at the Upper Limits of Performance?
Two top-performing face recognition systems from the Face Recognition Vendor Test-ongoing performed the same test (7).
Identity masking effectiveness and gesture recognition: Effects of eye enhancement in seeing through the mask
We evaluated the effectiveness of identity-masking algorithms based on Canny filters, applied with and without eye enhancement, for interfering with identification and preserving facial actions.
Twin identification over viewpoint change: A deep convolutional neural network surpasses humans
These findings also contribute to our understanding of DCNN performance for discriminating high-resemblance faces, demonstrate that the DCNN performs at a level at or above humans, and suggest a degree of parity between the features used by humans and the DCNN.
The Influence of the Other-Race Effect on Susceptibility to Face Morphing Attacks
Similar to humans, the DCNN performed more accurately for original face images than for morphed image pairs.
Face Identification Proficiency Test Designed Using Item Response Theory
Multiple tests of equal difficulty can be constructed then using subsets of items.
Single Unit Status in Deep Convolutional Neural Network Codes for Face Identification: Sparseness Redefined
Therefore, distributed and sparse codes co-exist in the network units to represent different face attributes.
Accuracy comparison across face recognition algorithms: Where are we on measuring race bias?
We discuss data driven factors (e. g., image quality, image population statistics, and algorithm architecture), and scenario modeling factors that consider the role of the "user" of the algorithm (e. g., threshold decisions and demographic constraints).
Evaluating the Effectiveness of Automated Identity Masking (AIM) Methods with Human Perception and a Deep Convolutional Neural Network (CNN)
Subjects were tested subsequently on their ability to recognize those identities in low-resolution videos depicting the drivers operating a motor vehicle.
Deep Convolutional Neural Networks in the Face of Caricature: Identity and Image Revealed
Deep convolutional neural networks (DCNNs) also create generalizable face representations, but with cascades of simulated neurons.
Face recognition assessments used in the study of super-recognisers
These tests have been used commonly in recent years to gauge the skills of perspective 'super-recognisers' with respect to the general population.
Neurons and Cognition
Deep Convolutional Neural Network Features and the Original Image
The results show that the DCNN features contain surprisingly accurate information about the yaw and pitch of a face, and about whether the face came from a still image or a video frame.
