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Found 11 papers, 2 papers with code
Enhancing Privacy in Face Analytics Using Fully Homomorphic Encryption
These features denote embeddings in latent space and are often stored as templates in a face recognition system.
RidgeBase: A Cross-Sensor Multi-Finger Contactless Fingerprint Dataset
Contactless fingerprint matching using smartphone cameras can alleviate major challenges of traditional fingerprint systems including hygienic acquisition, portability and presentation attacks.
HEFT: Homomorphically Encrypted Fusion of Biometric Templates
This paper proposes a non-interactive end-to-end solution for secure fusion and matching of biometric templates using fully homomorphic encryption (FHE).
Efficient Encrypted Inference on Ensembles of Decision Trees
In this work, we propose a framework to transfer knowledge extracted by complex decision tree ensembles to shallow neural networks (referred to as DTNets) that are highly conducive to encrypted inference.
Efficient CNN Building Blocks for Encrypted Data
Using the CKKS scheme available in the open-source HElib library, we show that operational parameters of the chosen FHE scheme such as the degree of the cyclotomic polynomial, depth limitations of the underlying leveled HE scheme, and the computational precision parameters have a major impact on the design of the machine learning model (especially, the choice of the activation function and pooling method).
Diversity in Faces
We expect face recognition to work equally accurately for every face.
Cultural Vocal Bursts Intensity Prediction
Face Recognition
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Understanding Unequal Gender Classification Accuracy from Face Images
Recent work shows unequal performance of commercial face classification services in the gender classification task across intersectional groups defined by skin type and gender.
Recognizing Disguised Faces in the Wild
While upcoming algorithms continue to achieve improved performance, a majority of the face recognition systems are susceptible to failure under disguise variations, one of the most challenging covariate of face recognition.
Heterogeneity Aware Deep Embedding for Mobile Periocular Recognition
Mobile biometric approaches provide the convenience of secure authentication with an omnipresent technology.
Unravelling Robustness of Deep Learning based Face Recognition Against Adversarial Attacks
In this paper, we attempt to unravel three aspects related to the robustness of DNNs for face recognition: (i) assessing the impact of deep architectures for face recognition in terms of vulnerabilities to attacks inspired by commonly observed distortions in the real world that are well handled by shallow learning methods along with learning based adversaries; (ii) detecting the singularities by characterizing abnormal filter response behavior in the hidden layers of deep networks; and (iii) making corrections to the processing pipeline to alleviate the problem.
