Acquiring/Recognising Eyes TRIZ Cases

Directional filters segment eye regions from noisy facial images to determine precise iris geometry.

A driver attentiveness detection device analyzes eye configurations against stored models to identify inattentive states.

Visibility limiting devices constrain angular fields of view to guide user gaze, reducing positioning time across vertically displaced biometric units.

Radon-Hough transformations analyze edge angles to detect eyeglasses, configuring tracking routines for glare and visibility issues.

A multimodal biometric system captures iris and face images using coordinated imaging subsystems.

A process converts disparate biometric formats into a common representation using feature-to-feature mapping functions for cross-vendor matching.

A display control device adjusts push information presentation styles based on calculated selection probabilities to prioritize user-relevant content.

A camera system captures user images within a defined interaction zone to enable biometric authentication without manual input.

An on-aircraft computer system analyzes involuntary eye movements to identify pilot attention tunneling.

An identity verification apparatus analyzes eye tracking data to detect blinking movements for user authentication.

A reduced attention state estimation system calculates microsaccade sharpness and eyelid opening standard deviation from eye movement data.

A gaze path analysis system extracts unique time-series eye movement patterns to verify user identity without display guides.

A driver gaze tracking system detects facial feature points to estimate head pose and calculate gaze direction for real-time monitoring.

Detects synthetic media by analyzing eye reflections of dynamic screen objects, preventing impersonation without heavy computation.

Alternating illumination sources shift eyeglass glare between captured images.

Bi-ocular coupling guides user gaze for self-administered retinal imaging, eliminating mechanical controls and trained operator requirements.

Blurred video capture reduces privacy concerns and intrusiveness while maintaining measurement precision for panelist identification.

A separation unit divides luminance information into intrusion and object components to generate output images.

A shield cover with a light passage hole positions illumination parts behind the front surface to block reflected light and reduce image noise.

Segmented face processing applies geometry and texture variations to obscure identity while preserving human recognizability.

A drowsiness estimation device uses infrared imaging to detect ocular region temperature for physiological state monitoring.

A liquid lens adjusts focal position via voltage while an imaging device captures sequential images.

A terminal loss detection system compares user feature information against stored data to identify unauthorized access attempts.

A near-to-eye display system senses operator head orientation to determine gaze direction and generate selective alert signals.

A detection method classifies blink events and instrument glances by analyzing eye opening width values against defined thresholds.

Pulsed light sources synchronized with camera shutters resolve the trade-off between high-resolution capture volume and system complexity.

Sequential multi-wavelength illumination captures iris texture and anti-spoofing data, resolving accuracy versus complexity trade-offs.

Local quality measurements prioritize textured regions, reducing noise from artifacts and improving reliability.

An identification model generation apparatus infers eye feature amounts from near-infrared images and capture status data to produce accurate authentication models.

An eye tracking device detects viewer gaze to adapt immersive video resolution across display zones.

Storing original image regions as metadata allows users to revert red-eye corrections on any device, resolving the inability to undo changes after transfer.

Electronic device converts captured biometric images into virtual objects to prevent sensitive data leakage during authentication.

Face tracking and morphing reduce bandwidth usage while maintaining visual quality, resolving the trade-off between bit rate and image fidelity.

Segmented calibration modes reduce positional deviation while minimizing operation time and complexity.

Analyzing curved features and edge maps, the system classifies lens types to resolve iris obstruction issues.

Infrared camera detects blood volume pulsation in facial regions to resolve the trade-off between measurement precision and ease of operation.

A drowsiness detection device calculates eye closure ratios and sets personalized thresholds based on initial driver patterns.

Optical coherence tomography targets specific biometric regions, reducing detection time and resource consumption across the field of view.

Electromyography electrodes capture muscle signals to detect facial expressions, bypassing accuracy loss from face obscuration by virtual reality hardware.

An ophthalmoscope uses infrared and visible light beams to induce natural pupil dilation without chemical agents.

A smartphone system captures pupillary and corneal light reflex images for infant eye screening.

A cross-sensor iris matching system normalizes images using modulation transfer functions to align disparate sensor characteristics.

Tracking arm and eye movements with a binocular camera predicts touch positions via an ECO-Lite neural network, reducing interaction delays.

A bandpass filter selects 920 to 1,500 nm wavelengths to reduce reflection noise from sunlight during outdoor iris recognition.

Aligning a customized 3D eye model with current images determines six degrees of freedom, compensating for corneal surface changes during ablation surgery.

An image preprocessing method sets edge lines and calculates energy values to adaptively crop input images.

A system calculates blood vessel density scores from eye images to predict fatigue.