Liveness Detection

The system prompts the user to perform a random, unpredictable action to verify liveness in real-time.

• Randomized Head Movement: The system asks the user to turn their head left, right, up, or down in a specific sequence, analyzing the resulting 3D parallax and perspective shift.
• Eye Blink Detection: Prompts the user to blink, measuring the natural duration (typically 100-400ms), speed, and completeness of eyelid closure.
• Smile or Expression Prompts: Requests a specific facial expression to confirm the subject can voluntarily manipulate facial muscles.
• Reading a Random Passphrase: Combines liveness with lip-reading analysis to match visemes to the expected phonemes of the challenge text.

Analyzes the pixel-level characteristics of the presented image without requiring user interaction, detecting artifacts introduced by spoofing media.

• Moiré Pattern Detection: Identifies the aliasing interference patterns created when a digital screen displaying a face is re-captured by the authentication camera.
• Print Artifact Analysis: Detects the microscopic paper texture, inkjet dot patterns, or gloss reflections characteristic of printed photographs.
• Color Space Inconsistency: Measures the reduced color gamut and dynamic range of a display or printed photo compared to the natural reflectance of human skin.
• Frequency Domain Anomalies: Transforms the image to the frequency domain to detect the grid-like peaks from LCD pixel structures or the sharp cutoff of printer halftoning.

Detects the subtle, unconscious physiological processes that are inherently present in a living human but absent or unnatural in artifacts.

• Photoplethysmography (PPG): Extracts minute, periodic skin color variations caused by blood volume changes during the cardiac cycle, verifying the presence of a heartbeat from video alone.
• Eye Micro-Saccades: Tracks the tiny, involuntary, high-frequency jitter of the human eye during fixation, a motion that static images and most deepfakes fail to replicate.
• Natural Blink Rhythm: Monitors the spontaneous, non-prompted blink rate and timing, which follows a stochastic pattern distinct from the absent or unnaturally regular blinking in spoofs.
• Subtle Head Sway: Detects the unconscious, low-amplitude postural sway of a living person holding a device, a motion signature absent in mounted artifacts.

Leverages sensor hardware or monocular depth estimation to verify the presented face occupies a three-dimensional volume with correct geometry.

• Structured Light / Time-of-Flight: Uses dedicated infrared depth sensors to project a dot pattern or measure light pulse return time, building a 3D point cloud that a flat photo cannot produce.
• Stereo Depth from Dual Cameras: Computes a disparity map from two slightly offset camera views to measure true depth, instantly revealing the planar surface of a screen or print.
• Monocular Depth Estimation: Uses a trained neural network to estimate a depth map from a single RGB image, checking for the flat, uniform depth of a mask or screen versus the complex curvature of a face.
• Specular Reflection Consistency: Analyzes the position and shape of light reflections on the cornea and skin, which will be physically inconsistent on the concave/convex surfaces of a 3D mask.

Evaluates the coherence of motion and appearance across a sequence of video frames to identify replay attacks and synthetic video injections.

• Optical Flow Field Analysis: Computes the motion vectors between consecutive frames, detecting the unnatural, perfectly smooth or jittery motion patterns of synthetic faces versus natural human dynamics.
• Video Replay Detection: Identifies the tell-tale signs of a screen recapture, including frame rate mismatches, bezel edge detection, and the subtle flicker of screen refresh rates.
• Background Consistency Check: Analyzes the stability and texture of the background behind the subject, flagging static backgrounds that are perfectly still (indicating a photo) or looped backgrounds in video replays.
• Compression Artifact Analysis: Detects the double-compression fingerprints left when a genuine video is re-encoded for replay, revealing a mismatch between the expected and observed quantization tables.

Binds the authentication event to the specific physical environment and moment in time to prevent pre-recorded or synthetic injection attacks.

• Challenge-Response Reflection: Projects a rapidly changing, random light pattern onto the user's face and verifies the expected reflection response, impossible to pre-compute for a replay attack.
• Device Motion Correlation: Correlates the motion signals from the device's IMU (accelerometer/gyroscope) with the visual motion in the camera feed to ensure the camera is viewing a live 3D scene.
• Ambient Light Analysis: Measures the ambient light color and intensity to verify it matches the illumination observed on the subject's face, detecting screen-based replays with different lighting environments.
• Timestamp & Nonce Verification: Cryptographically binds a server-generated, single-use nonce to the captured biometric sample to prevent the injection of a previously recorded, valid authentication session.