Electronic Device Non-Contact Recognition Pattern Adaptation
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Solution Overview
Problem
Non-contact type recognition technologies in electronic devices, such as facial and iris recognition, face challenges in accurately extracting user-specific information due to default recognition criteria set at manufacture, which fail to account for individual variations, leading to poor recognition rates.
Innovation Solution
A method and electronic device that updates default recognition criteria by collecting and analyzing user-specific patterns through a camera, determining effective values from candidate groups, and applying these to predefined defaults, thereby improving recognition performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If default recognition criteria are used for non-contact type recognition, then the device can operate immediately after manufacture, but the recognition rate deteriorates due to inability to reflect individual user characteristics
Solution Approach 1:
The system performs preliminary collection of user-specific recognition patterns during initial device setup and subsequent usage. The control unit accumulates user images and extracts recognition patterns (such as facial features, iris patterns, or hand gestures) to build a personalized database before formal recognition operations begin, thereby preparing individualized criteria in advance.
Solution Approach 2:
The recognition criteria transition from static default values to dynamic user-specific patterns. The control unit continuously updates the recognition database by comparing new user images with existing patterns, adapting the recognition standards to match the actual user's characteristics over time, making the system flexible and personalized.
2Measurement precision
If user-specific recognition patterns are collected and analyzed, then recognition accuracy improves, but device complexity increases due to additional data collection and processing requirements
Solution Approach 1:
The camera serves multiple functions: it captures images for both standard device operations and recognition pattern collection. The control unit performs dual roles by managing both general device control and specialized recognition pattern analysis, extracting features and updating databases using existing processing capabilities, thereby avoiding dedicated complex hardware.
Solution Approach 2:
The system automatically collects user images during normal operation and autonomously processes these images to extract recognition patterns. The control unit self-manages the entire workflow from image capture to pattern extraction and database updates without requiring manual intervention or complex external processing systems, making the complexity management self-contained.
Data Source
AI summary
A method for improving the performance of recognition by increasing the rate of recognition include executing a specific application, acquiring a user image from a camera while the specific application is executed, collecting a candidate group of non-contact type recognition patterns for an execution screen of the specific application by recognizing at least one of a user's face, eye and hand from the user image, determining an effective value from the collected candidate group of non-contact type recognition patterns, and updating a predefined default value by applying the determined effective value to the predefined default value. An electronic device for improving the performance of recognition and other embodiments also are disclosed.