3D Living Face Anti-Spoofing Sample with Indoor and Outdoor Scenes

Presenting a sample of 1,417 people for 3D living face and anti-spoofing analysis. It includes data from indoor and outdoor scenes, captured using iPhone X and iPhone XR devices, with subjects ranging from juvenile to elderly.

Use Cases

• Train a 3D face anti-spoofing model using data from indoor and outdoor scenes to improve robustness.
• Analyze the effectiveness of anti-spoofing techniques across different age groups, from juvenile to elderly.
• Evaluate model performance on facial data captured with specific mobile devices like iPhone X and iPhone XR.
• Study the impact of various facial expressions on anti-spoofing detection algorithms.

Strengths

• Contains 1,417 individual subjects, providing a sample for model development.
• Includes data diversity from both indoor and outdoor collection scenes.
• Covers a demographic range from juvenile to elderly, with a majority of young and middle-aged individuals.

Limitations

• The dataset is explicitly described as a sample of a larger paid dataset, limiting its scope.
• Specific column definitions, file formats, and data size are unknown, hindering precise technical assessment.
• Device diversity is limited to two iPhone models (X and XR), which may not generalize to other hardware.

Provenance

Source

Nexdata

Collection Method

Data collection involved capturing 3D living face and anti-spoofing data using iPhone X and iPhone XR devices in indoor and outdoor scenes.

Time Range

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Freshness

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Geography

Region tag indicates 'us', but specific geographic coverage within the description is not detailed.