AI Face Recognition With Dynamic User Feedback Correction

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Solution Overview

Problem

Conventional face recognition systems for multiple users often experience recognition errors, which cannot be easily corrected by users, leading to inconvenience in real-time applications due to their closed nature and inability to update personal data.

Innovation Solution

A processing method using an AI module that extracts embedding feature information from face images, provides recognition results with distance information, and allows users to correct errors by selecting from candidate names, updating the database accordingly, thereby improving recognition accuracy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If a closed face recognition system is used to ensure safety, then security is improved, but user convenience and ability to correct errors deteriorates

Engineering Contradiction:

ImprovesecurityVSAvoiduser convenience

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The system provides feedback to users when recognition errors occur by displaying candidate names and allowing users to correct mistakes. This feedback mechanism enables users to interact with the system and correct errors without compromising security, resolving the contradiction between security and user convenience.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system allows users to self-correct recognition errors through an intuitive interface where users can select from candidate names. This self-service capability gives users control over their own data without requiring system administrator intervention, maintaining both security and convenience.

Inventive Principle:

Principle #25Self-service

2Stability of the object's composition

If a closed face recognition system is used to maintain system stability, then system stability is improved, but ability to update personal data and correct errors deteriorates

Engineering Contradiction:

Improvesystem stabilityVSAvoidability to update data

Core Design Contradiction:

Stability of the object's compositionVSAdaptability or versatility

Solution Approach 1:

The system introduces dynamic updates to the face recognition database through user feedback. While the core recognition model remains stable, the system allows flexible updates to personal data and recognition models based on user corrections, achieving both stability and adaptability.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

User feedback mechanisms enable the system to learn from correction and update recognition models dynamically. This feedback loop allows the system to adapt to changing user needs and correct errors without destabilizing the core system architecture.

Inventive Principle:

Principle #23Feedback

3Measurement precision

If conventional face recognition methods are used to ensure accuracy, then recognition accuracy is improved, but ability to correct errors in real-time deteriorates

Engineering Contradiction:

Improverecognition accuracyVSAvoiderror correction time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system provides immediate feedback when recognition errors occur, displaying candidate names and allowing users to correct mistakes in real-time. This feedback mechanism eliminates the delay between error occurrence and correction, maintaining both accuracy and real-time responsiveness.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

Users can self-correct errors immediately without waiting for system administrator intervention or scheduled updates. The self-service correction mechanism enables real-time error correction, significantly reducing the time loss associated with conventional methods.

Inventive Principle:

Principle #25Self-service

4Reliability

If a closed face recognition system is used to maintain security, then security is improved, but ease of correcting recognition errors deteriorates

Engineering Contradiction:

ImprovesecurityVSAvoiderror correction ease

Core Design Contradiction:

ReliabilityVSEase of repair

Solution Approach 1:

The system enables users to self-correct recognition errors through an intuitive interface that displays candidate names and allows direct selection. This self-service approach makes error correction as easy as selecting from a list of names, maintaining both security and ease of correction.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The feedback mechanism provides users with immediate information about recognition confidence and candidate names, making error correction intuitive and easy. Users receive clear feedback about system uncertainty, enabling them to correct errors without technical knowledge while maintaining security protocols.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS11403875B2Processing method of learning face recognition by artificial intelligence module

Publication Date: 2022.08.02 ASKEY COMP

AI summary

A processing method of face recognition includes steps of: extracting embedding feature information from a face image; outputting a recognition result of face recognition according to the embedding feature information, wherein the recognition result includes a recognized name and embedding feature distance information; determining whether the recognized name is in a list or not; if the recognized name is in the list, performing a removal checking step for determining whether to remove the recognition result based on the embedding feature distance information; if determining that the recognition result is not to be removed, displaying the recognized name; if determining that the recognition result is to be removed, displaying a negative prompt; and dynamically and instantly providing a feedback and updating a recognition method for the face recognition.