Screen Wakeup Using Neural Network Face Gaze Detection

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Solution Overview

Problem

Existing screen wakeup technologies face challenges in achieving accurate screen activation without relying on high-quality pupil images, which are often affected by low-quality images from front-facing cameras, thereby increasing production costs.

Innovation Solution

A screen wakeup method utilizing a preconfigured neural network to analyze multiple image frames and determine whether the face image matches a preset face and belongs to a user gazing at the screen, thereby switching the screen from an off to an on state.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of manufacture

If a general front-facing camera is used to obtain pupil image for identification, then device cost is reduced, but identification accuracy deteriorates due to low image quality

Engineering Contradiction:
Improvedevice costVSAvoididentification accuracy
Core Design Contradiction:
Ease of manufactureVSMeasurement precision

Solution Approach 1:

The patent changes the identification parameters from pupil-based (requiring high-quality images) to face-based (working with lower-quality images). By using face image features instead of pupil features, the system can achieve accurate identification with standard front-facing cameras, resolving the contradiction between cost and accuracy

Inventive Principle:
Principle #35Parameter changes

Solution Approach 2:

The patent uses face image as a substitute (copy) for pupil image. Instead of requiring the original high-quality pupil image, the system creates an alternative identification pathway using face image data, which can be obtained from standard cameras without compromising identification functionality

Inventive Principle:
Principle #26Copying

2Measurement precision

If a special iris camera is used to obtain high-quality pupil image, then identification accuracy is improved, but device production cost increases

Engineering Contradiction:
Improveidentification accuracyVSAvoiddevice production cost
Core Design Contradiction:
Measurement precisionVSEase of manufacture

Solution Approach 1:

The patent extracts the identification function from the pupil image domain and relocates it to the face image domain. By separating the identification task from the requirement of high-quality pupil imaging, the system eliminates the need for expensive specialized cameras while maintaining identification accuracy

Inventive Principle:
Principle #2Taking out (Extraction)

Solution Approach 2:

The patent makes the front-facing camera universal by enabling it to perform both its original function (capturing face images for display/wake-up) and the additional function of providing identification data. This multi-functionality eliminates the need for separate specialized iris cameras, reducing device cost

Inventive Principle:
Principle #6Universality (Multi-functionality)

3Adaptability or versatility

If pupil image-based identification is used, then gaze detection capability is achieved, but image quality requirement increases device complexity

Engineering Contradiction:
Improvegaze detection capabilityVSAvoiddevice complexity
Core Design Contradiction:
Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent merges the wake-up function and identification function into a single integrated process using face images. By combining these functions and using the same face image data for both purposes, the system achieves gaze detection capability without requiring separate specialized hardware, thereby reducing device complexity

Inventive Principle:
Principle #5Merging (Combining)

Data Source

PatentEP3910507B1Method and apparatus for waking up screen
Publication Date: 2025.05.07 HUAWEI TECH CO LTD
  • EP3910507B1 patent drawingFigure 1
  • EP3910507B1 patent drawingFigure 2
  • EP3910507B1 patent drawingFigure 3A

AI summary

This application provides a screen wakeup method and apparatus. The screen wakeup method includes: obtaining M image frames, where each image frame includes a first face image, and M is an integer greater than or equal to 1; determining, based on a preconfigured neural network, whether each first face image matches a preset face image and belongs to a user who is gazing at a screen of a device; and when each first face image matches the preset face image and belongs to the user, switching the screen from a screen-off state to a screen-on state. According to the technical solutions provided in this application, accuracy of screen wakeup of a device can be improved without significantly increasing costs.