3D Face Anti-Spoofing Images from Indoor and Outdoor Scenes

Encompassing a sample of 3D face images for anti-spoofing research, featuring 10 individuals. Data was collected using iPhone X and iPhone XR devices across indoor and outdoor scenes, with subjects ranging from juvenile to elderly age groups.

Use Cases

• Train a binary classifier to distinguish between 'living face' and 'spoofing' samples using 3D facial data.
• Analyze the impact of different collection scenes (indoor vs. outdoor) on anti-spoofing model performance.
• Evaluate model robustness across a diversity of facial expressions and demographic attributes like age and gender.

Strengths

• Includes data from 10 distinct individuals, providing multiple subject samples.
• Covers both indoor and outdoor collection scenes, adding environmental variety.
• Contains a demographic range from juvenile to elderly, with a focus on young and middle-aged adults.

Limitations

• The dataset is explicitly described as a sample, indicating it is not the full paid dataset.
• The specific number of images, rows, or file size is unknown, limiting assessment of scale.
• Device diversity is limited to two iPhone models (iPhone X and iPhone XR).

Provenance

Source

Nexdata

Collection Method

Collected using iPhone X and iPhone XR devices; described as a sample of a paid dataset.

Freshness

Last updated on 2025-04-25.

Geography

Region tag indicates 'us', but specific spatial coverage is not detailed in the provided description.