Human Iris Images Dataset

Multi-Material Fingerprint Spoofing Dataset contains 4,000+ fingerprint images from 100 individuals, 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.

Biometric Fingerprint Spoofing Dataset contains 5,000+ high-quality fingerprint images capturing real fingerprints and multiple spoofing fingerprint 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 is a high-quality replay attack dataset containing 4,714 PC-recorded video clips of real faces, designed for training and evaluating face recognition and liveness detection systems. This anti-spoofing videos dataset includes diverse attack scenarios, technical metadata (age, gender, ethnicity), and MP4/MOV formats to support spoofing detection, biometric security, and computer vision model development.

This anti-spoofing dataset contains over 38,000 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.

Real vs Fake Human Voice – Deepfake Audio Dataset contains 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.

Kids Anti-Spoofing 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 500,000 annotated images collected from 50,000 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.

iBeta Kids Dataset 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.