Search Results for author:
Found 21 papers, 10 papers with code
Deep Pixel-wise Binary Supervision for Face Presentation Attack Detection
The proposed approach achieves an HTER of 0% in Replay Mobile dataset and an ACER of 0. 42% in Protocol-1 of OULU dataset outperforming state of the art methods.
Learning One Class Representations for Face Presentation Attack Detection using Multi-channel Convolutional Neural Networks
The proposed system is evaluated on the publicly available WMCA multi-channel face PAD database, which contains a wide variety of 2D and 3D attacks.
Ranked #1 on
Face Anti-Spoofing
on MLFP
DeepFakes: a New Threat to Face Recognition? Assessment and Detection
The best performing method, which is based on visual quality metrics and is often used in presentation attack detection domain, resulted in 8. 97% equal error rate on high quality Deepfakes.
EdgeFace: Efficient Face Recognition Model for Edge Devices
In this paper, we present EdgeFace, a lightweight and efficient face recognition network inspired by the hybrid architecture of EdgeNeXt.
Ranked #1 on
Lightweight Face Recognition
on CFP-FP
Cross Modal Focal Loss for RGBD Face Anti-Spoofing
Automatic methods for detecting presentation attacks are essential to ensure the reliable use of facial recognition technology.
On the Effectiveness of Vision Transformers for Zero-shot Face Anti-Spoofing
Although various methods have been suggested for detecting such attacks, most of them over-fit the training set and fail in generalizing to unseen attacks and environments.
Biometric Face Presentation Attack Detection with Multi-Channel Convolutional Neural Network
We also introduce the new Wide Multi-Channel presentation Attack (WMCA) database for face PAD which contains a wide variety of 2D and 3D presentation attacks for both impersonation and obfuscation attacks.
Ranked #2 on
Face Presentation Attack Detection
on WMCA
Prepended Domain Transformer: Heterogeneous Face Recognition without Bells and Whistles
The core idea of the proposed approach is to add a novel neural network block called Prepended Domain Transformer (PDT) in front of a pre-trained face recognition (FR) model to address the domain gap.
Domain Adaptation in Multi-Channel Autoencoder based Features for Robust Face Anti-Spoofing
The proposed system is tested on a very recent publicly available multi-channel PAD database with a wide variety of presentation attacks.
Can Your Face Detector Do Anti-spoofing? Face Presentation Attack Detection with a Multi-Channel Face Detector
In a typical face recognition pipeline, the task of the face detector is to localize the face region.
Ranked #1 on
Face Presentation Attack Detection
on WMCA
(ACER@0.2BPCER metric)
Deep Models and Shortwave Infrared Information to Detect Face Presentation Attacks
Conducted experiments show superior performance over similar models acting on either color images or on a combination of different modalities (visible, NIR, thermal and depth), as well as on a SVM-based classifier acting on SWIR image differences.
The High-Quality Wide Multi-Channel Attack (HQ-WMCA) database
The High-Quality Wide Multi-Channel Attack database (HQ-WMCA) database extends the previous Wide Multi-Channel Attack database(WMCA), with more channels including color, depth, thermal, infrared (spectra), and short-wave infrared (spectra), and also a wide variety of attacks.
A Comprehensive Evaluation on Multi-channel Biometric Face Presentation Attack Detection
Having a lot of sensors increases the cost of the system, and therefore an understanding of the performance of different sensors against a wide variety of attacks is necessary while selecting the modalities.
Fairness Index Measures to Evaluate Bias in Biometric Recognition
While few, existing fairness measures are based on post-decision data (such as verification accuracy) of biometric systems, we discuss how pre-decision data (score distributions) provide useful insights towards demographic fairness.
Residual Feature Pyramid Network for Enhancement of Vascular Patterns
The accuracy of finger vein recognition systems gets degraded due to low and uneven contrast between veins and surroundings, often resulting in poor detection of vein patterns.
Bridging the Gap: Heterogeneous Face Recognition with Conditional Adaptive Instance Modulation
Heterogeneous Face Recognition (HFR) aims to match face images across different domains, such as thermal and visible spectra, expanding the applicability of Face Recognition (FR) systems to challenging scenarios.
Toward responsible face datasets: modeling the distribution of a disentangled latent space for sampling face images from demographic groups
Recently, it has been exposed that some modern facial recognition systems could discriminate specific demographic groups and may lead to unfair attention with respect to various facial attributes such as gender and origin.
Vulnerability of Automatic Identity Recognition to Audio-Visual Deepfakes
From the publicly available speech dataset LibriTTS, we also created a separate database of only audio deepfakes LibriTTS-DF using several latest text to speech methods: YourTTS, Adaspeech, and TorToiSe.
ChatGPT and biometrics: an assessment of face recognition, gender detection, and age estimation capabilities
This paper explores the application of large language models (LLMs), like ChatGPT, for biometric tasks.
Heterogeneous Face Recognition Using Domain Invariant Units
Heterogeneous Face Recognition (HFR) aims to expand the applicability of Face Recognition (FR) systems to challenging scenarios, enabling the matching of face images across different domains, such as matching thermal images to visible spectra.
From Modalities to Styles: Rethinking the Domain Gap in Heterogeneous Face Recognition
In our work, we view different modalities as distinct styles and propose a method to modulate feature maps of the target modality to address the domain gap.
