Anti-Spoofing Replay PC Videos Dataset

High-quality iris dataset with 5,000+ JPG images from 5,000+ individuals, containing left and right eye captures for iris recognition and computer vision tasks. This eye iris dataset includes labeled images collected under visible light, with metadata (ID, eye, sex) to support training data for biometric recognition systems, iris segmentation, and iris pattern analysis.

This fingerprint spoofing dataset contains fingerprint images, captured with a ZKTeco ZK9500 optical scanner and including real fingerprints and spoofing attacks created with alginate, plasticine, and silicone materials. The fingerprint dataset includes metadata (gender, age, finger, hand, device) and supports biometric security research, presentation attack detection, spoof detection, and fingerprint recognition model training.

Contains 5,000+ high-quality fingerprint images capturing real fingerprints and multiple fingerprint spoofing attack types, including print and replay scenarios. Designed for spoofing detection and liveness detection tasks, the fingerprint dataset provides labeled biometric data from different devices and fingers to train and evaluate biometric security and fingerprint recognition systems.

This anti-spoofing dataset contains over 40,036 live facial video recordings captured on mobile devices to support replay attack detection and biometric anti-spoofing research. With paired video sets, MP4/MOV formats, and rich metadata such as age, gender, and ethnicity, it provides reliable training data for face antispoofing, liveness detection, and secure biometric authentication systems.

Audio dataset containing 5,000 audio files featuring both genuine human recordings and AI-generated voice samples. Each set includes four speakers with multiple clips across M4A and MP3 formats. The dataset supports research in deepfake detection, generated speech analysis, and real vs fake human voice recognition tasks.

The dataset provides 6,000 high-quality facial images of children aged 7–15 for face anti-spoofing and liveness detection tasks. This child safety dataset supports research in biometric systems, helping improve facial recognition accuracy, detect spoofing attacks, and build safer AI models for protecting kids in digital and identification environments.

This high-quality open palm dataset includes 179,052 annotated images collected from 29,842 people, with each set containing six palm photos, two printed-hand images, and two replay videos. Designed for hand recognition and computer vision research, it provides detailed metadata - age, gender, ethnicity, profession, device type, dominant hand, and jewelry status.

It is a synthetic Turkish passports dataset containing 5,000 high-quality, AI-generated images. Labeled with detailed metadata - including passport ID, class, gender, and lighting - this dataset supports PII extraction, identity verification, and biometric recognition system training while maintaining strict data protection standards.

It is a child safety dataset featuring over 46,000 short video samples of children across different age groups, recorded under varied lighting, devices, and conditions. It includes four main attack types: Real Person, 2D Mask, 3D Mask, and Replay, helping develop biometric systems that detect spoofing and ensure safe, accurate child identification.

This German passport dataset provides 5,000 AI-generated synthetic passport images, engineered for training and benchmarking ML models in document analysis and PII extraction. It features high-resolution JPG samples with controlled variations across 3 angles, 4 lighting conditions, and 4 backgrounds, each annotated with detailed metadata including passport ID, gender, and age group for robust model development.