Acquiring/Recognising Eyes TRIZ Cases

A cumulative-sum-based change point analysis generates pattern vectors from polar iris image cells.

Automated gaze analysis classifies driver eye movements to detect elevated workload states.

A near-infrared sensor employs a two-dimensional semiconductor layer paired with a hexagonal boron nitride tunneling barrier to enhance photo-responsivity.

Segmenting video fields reduces transmission load while maintaining detection reliability for dynamic applications.

Broadcasting a kiosk identifier retrieves specific reference images, reducing search space and processing time during high-traffic authentication.

A medical gaze tracker detects user eye movements in response to non-diagnostic stimuli to generate diagnostic indications.

A mobile authentication system aligns candidate images with stored enrollment templates to verify identity.

A face recognition system calculates pupil coordinates to generate normalized image templates for automated identification.

An object recognition processing apparatus correlates eye fixation locations with field of view images to classify features.

A wearable lens device integrates biometric scanning and GPS tracking to deliver secure augmented reality data access.

Polar segmentation identifies iris center points via correlation, eliminating complex contour detection and normalization steps.

An HMD replaces password entry with iris scanning to resolve security risks from unauthorized access while maintaining multi-user convenience.

A camera system captures face images alongside iris or eye-print features to verify user identity through multi-factor biometric analysis.

An ophthalmologic apparatus detects fundus sites using edge detection and template matching processors.

Gyroscope sensors detect human motion to trigger image capture and biological authentication, eliminating physical key damage.

Electronic device segments image into partial regions, displaying high-resolution areas for authentication while lowering power consumption.

A neural network analyzes user gaze coordinates to determine engagement levels with presented content items.

Dual infrared LEDs switch intensity levels to capture glint reflections and pupil data, resolving gaze direction accuracy limits in wearable systems.

A gaze target determination device generates display events to synchronize user eye movements with screen objects for accurate focus identification.

Non-uniform feature mark boxes enable bidirectional cross comparison of iris images, resolving accuracy drops caused by varying sunlight angles and intensities.

A multimodal tracking system synchronizes eye, body, and physiological data streams to detect performance delays.

An ocular sensor detects eye movement and pupil size changes to authenticate users without passwords.

Polar coordinate transformation segments iris patterns from distant, non-cooperative subjects to maintain recognition accuracy despite occlusions.

Multi-angle illumination and pixel processing reduce glare from corrective lenses, maintaining image quality despite head and eye motion.

Merging visible, infrared, and depth images resolves lighting variability and spoofing risks in biometric recognition.

Uses infrared illumination at 25 to 65 degrees from the line of sight to evaluate inflammatory processes without causing patient discomfort.

A dynamic vision sensor detects radial iris movements to distinguish real faces from static images, resolving authentication reliability issues.

Cross-region patterns on stereo images detect pupil centers, resolving red-eye effects and positional variations.

An enclosure hood blocks ambient light interference, ensuring accurate line-of-sight estimation by preventing unwanted glints on the cornea.

Separating the lighting unit from rotating assemblies prevents wire disconnection and heat transfer while maintaining optical alignment.

A behavior recognition method computes face-to-camera distance to determine maximum field of vision for accurate user intent detection.

An adaptive authentication framework combines NFC badge data with behavioral biometrics to resolve forgery vulnerabilities in static identification methods.

Angled near-infrared light sources reduce eyeglass reflection interference and shading effects during driver eye condition monitoring.

Merging mirrored eye region images creates a robust template that improves recognition accuracy in poor lighting.

An iris recognition assembly acquires raw images and removes eyelash interference using a stored template to improve accuracy.

An adaptive eye tracker selects between bright-pupil and dark-pupil imaging modes using dual cameras to ensure continuous operation.