Institute, Warsaw, Poland This paper describes the results of the IEEE Bigdata 2023 Keystroke Verification Challenge 1 (KVC), that considers the biometric verification performance of Keystroke Dynamics (KD), capture...
Institute, Warsaw, Poland This paper describes the results of the IEEE Bigdata 2023 Keystroke Verification Challenge 1 (KVC), that considers the biometric verification performance of Keystroke Dynamics (KD), captured as tweet-long sequences of variable transcript text from over 185,000 subjects. The data are obtained from two of the largest public databases of KD up to date, the Aalto Desktop and Mobile Keystroke databases, guaranteeing a minimum amount of data per subject, age and gender annotations, absence of corrupted data, and avoiding excessively unbalanced subject distributions with respect to the considered demographic attributes. Several neural architectures were proposed by the participants, leading to global Equal Error Rates (EERs) as low as 3.33% and 3.61% achieved by the best team respectively in the desktop and mobile scenario, outperforming the current state of the art biometric verification performance for KD. Hosted on Codalab 2 , the KVC will be made ongoing to represent a useful tool for the research community to compare different approaches under the same experimental conditions and to deepen the knowledge of the field.
This paper describes the experimental framework and results of the ICDAR 2021 Competition on On-Line Signature Verification (SVC 2021). The goal of SVC 2021 is to evaluate the limits of on-line signature verification ...
详细信息
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 (IJ...
详细信息
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 (IJ...
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). The competition attracted a total of 8 participating teams with valid submissions from academia and industry. The competition aimed to motivate and attract solutions that target detecting face presentation attacks while considering synthetic-based training data motivated by privacy, legal and ethical concerns associated with personal data. To achieve that, the training data used by the participants was limited to synthetic data provided by the organizers. The submitted solutions presented innovations and novel approaches that led to outperforming the considered baseline in the investigated benchmarks.
暂无评论