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Found 12 papers, 8 papers with code
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.
Ranked #1 on
Face Recognitio
on Pola thermal
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.
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.
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.
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
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.
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)
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
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.
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.
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.
