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Found 8 papers, 3 papers with code
PrivacyNet: Semi-Adversarial Networks for Multi-attribute Face Privacy
Further, PrivacyNet allows a person to choose specific attributes that have to be obfuscated in the input face images (e. g., age and race), while allowing for other types of attributes to be extracted (e. g., gender).
Some Research Problems in Biometrics: The Future Beckons
The need for reliably determining the identity of a person is critical in a number of different domains ranging from personal smartphones to border security; from autonomous vehicles to e-voting; from tracking child vaccinations to preventing human trafficking; from crime scene investigation to personalization of customer service.
FlowSAN: Privacy-enhancing Semi-Adversarial Networks to Confound Arbitrary Face-based Gender Classifiers
In this regard, Semi-Adversarial Networks (SAN) have recently emerged as a method for imparting soft-biometric privacy to face images.
ASD-DiagNet: A hybrid learning approach for detection of Autism Spectrum Disorder using fMRI data
The proposed approach is evaluated on a public dataset provided by Autism Brain Imaging Data Exchange including 1035 subjects coming from 17 different brain imaging centers.
Rank consistent ordinal regression for neural networks with application to age estimation
In many real-world prediction tasks, class labels include information about the relative ordering between labels, which is not captured by commonly-used loss functions such as multi-category cross-entropy.
Ranked #1 on
Age Estimation
on AFAD
Spoofing PRNU Patterns of Iris Sensors while Preserving Iris Recognition
The principle of Photo Response Non-Uniformity (PRNU) is used to link an image with its source, i. e., the sensor that produced it.
Gender Privacy: An Ensemble of Semi Adversarial Networks for Confounding Arbitrary Gender Classifiers
Recent research has proposed the use of Semi Adversarial Networks (SAN) for imparting privacy to face images.
Semi-Adversarial Networks: Convolutional Autoencoders for Imparting Privacy to Face Images
In this paper, we design and evaluate a convolutional autoencoder that perturbs an input face image to impart privacy to a subject.
