Acquiring/Recognising Eyes TRIZ Cases

Dynamic display luminance adjustment controls pupil diameter, ensuring accurate iris authentication in varying outdoor illumination conditions.

Mirrored visual feedback guides users to align the optical axis, resolving the contradiction between security performance and ease of operation.

A lens module integrates a holographic optical element with refractive lenses to form an iris recognition camera system.

A 3D gaze tracker combines time-of-flight and picture cameras to determine gaze vectors without head stabilization.

P-polarized light incident at Brewster's angle reduces unwanted reflections from glasses, enabling accurate pupil identification.

A head-mounted device adjusts optical assemblies using a user-specific model derived from facial geometry scans.

A single sensor captures facial and iris images by adjusting illumination and exposure settings between acquisitions.

A driver assessment system uses cameras and deep neural networks to classify head poses and eye gaze directions for real-time monitoring.

A processor estimates a user's visually recognized range to specify objects and collect related information based on identified attributes.

A front-facing camera captures palm images while the device screen emits light to improve image contrast.

A curved flexible resolution test chart couples to an artificial eye to evaluate fundus imaging system performance.

An eye movement sensor captures operator gaze data to identify unsafe drilling behaviors through automated analysis.

A video processing system adjusts eye orientation to simulate natural contact during calls.

A training template construction apparatus generates personalized gaze sequences using eye tracking data and machine learning algorithms.

Feature-based blur estimation uses iris-pupil edge geometry to predict eye motion and generate de-blurred images for biometric identification.

Electronic device stores multiple reference iris templates under varying illumination conditions to maintain recognition accuracy despite pupil size changes.

Dynamic lighting and multiple cameras improve image quality for biometric authentication despite system complexity.

A system captures pupil dilation responses to visual challenge prompts for user authentication.

A vehicle-mounted controller correlates driver gaze coordinates with external scene targets to detect distracted driving behavior.

An automated conversion engine adapts eye-tracking features data to device-specific sampling rates for accurate assessment model deployment.

Digitizing the iris edge as a fixed landmark stabilizes eye geometry tracking, resolving micro-tremor precision limits caused by pupil size changes.

Feature vector clustering processes high-resolution eye tracking data to resolve the trade-off between measurement precision and data volume.

A data processor calculates eyelid amplitude-to-velocity ratios to determine operator alertness levels.

A dual imaging sensor captures overlapping body part images to generate a precise three-dimensional biometric model.

Phase modulation extends depth of field while maintaining spatial frequencies, resolving aperture size versus signal-to-noise ratio trade-offs.

An image processing device validates eyelid outline candidate lines against normal blink movement patterns to reduce false detection in face images.

An eye tracking device monitors driver gaze location to dynamically adjust indicator prominence on vehicle displays.

Segmenting comparison data into matching and non-matching zones clarifies authentication validity while managing processing complexity.

A facial recognition system analyzes corneal reflections to verify biometric authenticity.

A preview apparatus determines a color image from facial data for iris recognition.

Dual illuminator scheme detects corneal specular reflections to locate the eye region for authentication.

A precision timing control mechanism coordinates subframe exposures across multiple active infrared cameras to enable interleaved operation.

A motion recognition apparatus segments operator movements by body part to generate individual start and end times for each limb.

A processing system extracts corneal light reflection features from a single image to perform biometric detection without multiple camera inputs.

Asymmetric tracking reduces computational load by estimating the second eye's position instead of calculating full depth maps.

Optical facial replicas simulate natural eye movements to deceive vehicle monitoring systems.

Dynamic illumination positioning shifts specular reflections off the iris, eliminating false negatives and allowing recognition without removing spectacles.

A liveness verification system selects models based on color and infrared image patches to detect spoofing artifacts.

A bilateral iris imaging apparatus rotates segmented eye images to zero tilt using interpupillary distance data.

A mobile eye imaging system captures retinal images and determines image quality using machine learning algorithms.

Infrared LEDs illuminate eyes so a standard video camera analyzes pupil roundness for accurate gaze detection without bulky specialized hardware.

Integrates an iris camera and infrared source into a virtual reality housing to capture biometric images through the display lens.

Segmenting detection into speed-optimized and analysis-optimized stages reduces click-to-click time while maintaining high accuracy.

Dynamic rendering increases bitrate in regions of interest detected via pupil size, resolving bandwidth constraints while maintaining user experience.

A Z-dimension user-feedback system modifies image brightness to guide subject positioning.

Segmenting wide and narrow fields reduces processing latency while maintaining high precision in distant gaze detection.

A pupil detection apparatus uses dual imaging pairs with varying separation distances to capture near-infrared eye images.

Selective pulse wave detection reduces processing load while maintaining measurement precision for driver monitoring.