Search Results for author:
Found 7 papers, 2 papers with code
Second Edition FRCSyn Challenge at CVPR 2024: Face Recognition Challenge in the Era of Synthetic Data
Synthetic data is gaining increasing relevance for training machine learning models.
SDFR: Synthetic Data for Face Recognition Competition
The submitted models were trained on existing and also new synthetic datasets and used clever methods to improve training with synthetic data.
Model Pairing Using Embedding Translation for Backdoor Attack Detection on Open-Set Classification Tasks
Backdoor attacks allow an attacker to embed a specific vulnerability in a machine learning algorithm, activated when an attacker-chosen pattern is presented, causing a specific misprediction.
FRCSyn Challenge at WACV 2024:Face Recognition Challenge in the Era of Synthetic Data
Despite the widespread adoption of face recognition technology around the world, and its remarkable performance on current benchmarks, there are still several challenges that must be covered in more detail.
An anomaly detection approach for backdoored neural networks: face recognition as a case study
Backdoor attacks allow an attacker to embed functionality jeopardizing proper behavior of any algorithm, machine learning or not.
DMD: A Large-Scale Multi-Modal Driver Monitoring Dataset for Attention and Alertness Analysis
Vision is the richest and most cost-effective technology for Driver Monitoring Systems (DMS), especially after the recent success of Deep Learning (DL) methods.
Real-Time Driver State Monitoring Using a CNN Based Spatio-Temporal Approach
Experiments show that our approach outperforms the state-of-the art results on the Distracted Driver Dataset (96. 31%), with an accuracy of 99. 10% for 10-class classification while providing real-time performance.
