VAMONOT_S is the synthetic counterpart of VAMONOT_R and has been developed to provide a controlled, privacy-preserving benchmark for evaluating biometric systems under realistic verification conditions.
Description
The dataset consists of synthetic video sequences generated using tools based on Hailuo AI.
The videos are designed to simulate realistic identity verification scenarios, including:
- Controlled verification scenarios, similar to an airport security checkpoint, where the subject walks toward a fixed camera while maintaining a frontal view.
- Less constrained scenarios, characterized by natural head and body movements.
VAMONOT_S is built from 55 synthetic identities sourced from the ONOT synthetic dataset of ICAO-compliant facial images.
Data Organization
VAMONOT_S follows the same core structure as VAMONOT_R, based on the comparison between a document image and a live video sequence.
The content of the dataset is organized as follows:
- The whole dataset is divided into 55 folders, one folder for each subject
- Each subject folder contains:
- icao: the folder with the ISO/ICAO compliant image
- morphed: the folder with morphed images
- videos: the folder with videos
- video_gate: controlled scenario, frontal position of the subject
- video_gate_cappello_occhiali: less controlled scenario, subject with garments
- video_in_the_wild: more challenging scenario, with variable head poses, camera positions and garments
Each video folder has the following structure:
- face_detection: detected faces (computed using MediaPipe)
- frames: original frames of the video (a frame was sampled every 2)
- landmarks: annotations and images with landmarks (computed using MediaPipe)
- ofiq: face quality score computed using the OFIQ framework
Citation
When using VAMONOT_S in your research, please cite the corresponding publication: