Search Results for author:
Found 25 papers, 5 papers with code
Impact of Synthetic Images on Morphing Attack Detection Using a Siamese Network
Our results show that MAD trained on EfficientNetB0 from FERET, FRGCv2, and FRLL can reach a lower error rate in comparison with SOTA.
Double Trouble? Impact and Detection of Duplicates in Face Image Datasets
Additional steps based on face recognition and face image quality assessment models reduce false positives, and facilitate the deduplication of the face images both for intra- and inter-subject duplicate sets.
SynFacePAD 2023: Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data
This paper presents a summary of the Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data (SynFacePAD 2023) held at the 2023 International Joint Conference on Biometrics (IJCB 2023).
Iris Liveness Detection Competition (LivDet-Iris) -- The 2023 Edition
New elements in this fifth competition include (1) GAN-generated iris images as a category of presentation attack instruments (PAI), and (2) an evaluation of human accuracy at detecting PAI as a reference benchmark.
Towards minimizing efforts for Morphing Attacks -- Deep embeddings for morphing pair selection and improved Morphing Attack Detection
The results endorse the advantages of face embeddings in more effective image pre-selection for face morphing and accurate detection of morphed face images.
Flickr-PAD: New Face High-Resolution Presentation Attack Detection Database
This will help researchers to compare new approaches to existing algorithms on a wider database.
Face Feature Visualisation of Single Morphing Attack Detection
This paper proposes an explainable visualisation of different face feature extraction algorithms that enable the detection of bona fide and morphing images for single morphing attack detection.
Considerations on the Evaluation of Biometric Quality Assessment Algorithms
Additionally, a discard fraction limit or range must be selected to compute pAUC values, which can then be used to quantitatively rank quality assessment algorithms.
Effect of Lossy Compression Algorithms on Face Image Quality and Recognition
This work investigates the effect of lossy image compression on a state-of-the-art face recognition model, and on multiple face image quality assessment models.
Improving Presentation Attack Detection for ID Cards on Remote Verification Systems
In this paper, an updated two-stage, end-to-end Presentation Attack Detection method for remote biometric verification systems of ID cards, based on MobileNetV2, is presented.
Learning to Predict Fitness for Duty using Near Infrared Periocular Iris Images
This research proposes a new database and method to detect the reduction of alertness conditions due to alcohol, drug consumption and sleepiness deprivation from Near-Infra-Red (NIR) periocular eye images.
Alcohol Consumption Detection from Periocular NIR Images Using Capsule Network
This research proposes a method to detect alcohol consumption from Near-Infra-Red (NIR) periocular eye images.
Single Morphing Attack Detection using Siamese Network and Few-shot Learning
This paper proposes a framework following the Few-Shot-Learning approach that shares image information based on the siamese network using triplet-semi-hard-loss to tackle the morphing attack detection and boost the clustering classification process.
A Novel Capsule Neural Network Based Model for Drowsiness Detection Using Electroencephalography Signals
Due to the transient mental state of a human subject between alertness and drowsiness, automated drowsiness detection is a complex problem to tackle.
Towards an Efficient Semantic Segmentation Method of ID Cards for Verification Systems
The best results for the fused multi-country dataset of ID Card images from Chile, Argentina and Mexico reached an IoU of 0. 9911.
Single Morphing Attack Detection using Feature Selection and Visualisation based on Mutual Information
Face morphing attack detection is a challenging task.
Synthetic Periocular Iris PAI from a Small Set of Near-Infrared-Images
Such results demonstrated the feasibility of synthetic images to fool presentation attacks detection algorithms and the need for such algorithms to be constantly updated and trained with a larger number of images and PAI scenarios.
Semantic Segmentation of Periocular Near-Infra-Red Eye Images Under Alcohol Effects
The purpose of the system is to measure the fitness for duty.
Iris Liveness Detection using a Cascade of Dedicated Deep Learning Networks
The bona fide class consists of live iris images, whereas the attack presentation instrument classes are comprised of cadaver, printed, and contact lenses images, for a total of four scenarios.
Selfie Periocular Verification using an Efficient Super-Resolution Approach
Selfie-based biometrics has great potential for a wide range of applications since, e. g. periocular verification is contactless and is safe to use in pandemics such as COVID-19, when a major portion of a face is covered by a facial mask.
Iris Liveness Detection Competition (LivDet-Iris) -- The 2020 Edition
Launched in 2013, LivDet-Iris is an international competition series open to academia and industry with the aim to assess and report advances in iris Presentation Attack Detection (PAD).
Sex-Prediction from Periocular Images across Multiple Sensors and Spectra
In this paper, we provide a comprehensive analysis of periocular-based sex-prediction (commonly referred to as gender classification) using state-of-the-art machine learning techniques.
Gender Classification from Iris Texture Images Using a New Set of Binary Statistical Image Features
This paper explores the use of a Binary Statistical Features (BSIF) algorithm for classifying gender from iris texture images captured with NIR sensors.
Relevant features for Gender Classification in NIR Periocular Images
In this work, we analyze and demonstrate the location of the most relevant features that describe gender in periocular NIR images and evaluate its influence its classification.
Sex-Classification from Cell-Phones Periocular Iris Images
These results compare well with the state of the art and show that when improving image resolution with the SRCNN the sex-classification rate increases.
