Single Image Liveness Detection
Liveness detection in just a single frame
Get the most friction-less liveness detection experience using a single image.
What is Single Image Liveness Detection?
Single Image Liveness is a biometric security mechanism that uses machine learning algorithms to verify whether a captured image was taken on the spot from the live person, or is a spoof (such as a photograph, video, or mask).
This allows for much less user-friction compared to other forms of liveness, increasing the pass rate of genuine customers.
Liveness DetectionThe Single Image Liveness Detection Process
The system captures a single image from the user, then analyzes multiple metrics,
including but not limited to:
Edge Detection
Images printed on paper can have sharp transitions or changes in pixel intensity, which can be detected.
Color Spectrum Analysis
Non-real faces can have color inaccuracies, limited ranges, or unnatural hues due to different limitations.
Light Pattern Analysis
Images captured form a screen can produce reflections and/or glares, detectable through light pattern analysis.
Why Use Single Image Liveness?
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Frequently Asked Questions
Single-image liveness detection is a biometric technique that utilizes a single retained image to check if the person is real or fake. However, it examines the visual sample to distinguish the real faces from spoofed attacks.
It depends on the artificial intelligence algorithms to check image aspects, for instance, texture, lighting, alongside facial symmetry. Also, these features assist in recognizing the irregularities in spoofed images or deepfakes.
Definitely, latest single-image liveness detection can disclose the irregularities in deepfake-generated photos. It highlights slight changes or mismatches that don't appear in the live image.
It protects attacks while using the images, videos, masks, or deepfake technology by examining the image’s reality. This assists in saving the systems from various facial spoofing.
Latest systems retain the high precision rates, usually exceeding up to 95%. Furthermore, potency changes depend on algorithm sophistication and attack complexity.
