Face Detection System for Unspecified Person Categorization
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Solution Overview
Problem
Existing face detection systems struggle to categorize and monitor individuals entering and exiting buildings like stores, hospitals, and schools, where a large number of unspecified persons frequent, as they require pre-registration for permission determination.
Innovation Solution
A face detection method implemented by a camera and server system that captures video, detects faces, generates face cut-out images, counts detection occurrences, and determines categories based on detection frequency, allowing for efficient monitoring and alert notifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If pre-registration is required for permission determination, then entry permission control is reliable, but the system cannot handle large numbers of unspecified persons entering and exiting buildings
Solution Approach 1:
The system performs preliminary actions by registering persons and determining their categories (such as employees, visitors, or suspicious persons) before actual permission determination is needed. Face images are captured and stored in advance, and categories are predetermined based on detection frequency patterns, enabling rapid permission decisions without requiring real-time pre-registration during entry events.
Solution Approach 2:
The system dynamically adjusts permission determination based on detected patterns of person detection frequencies. Rather than static pre-registration requirements, the system adapts by learning from detection data over time, automatically categorizing persons based on their detection patterns and updating permission status dynamically without manual re-registration.
2Measurement precision
If face detection is performed on all persons in captured video, then monitoring coverage is complete, but processing time and computational resources increase
Solution Approach 1:
The system extracts only the essential element needed for identification - the face region - from the entire captured video frame. By isolating and processing only face areas rather than analyzing all video content, the system maintains detection accuracy while significantly reducing processing time and computational resources required for monitoring all persons.
Solution Approach 2:
The system performs partial action by focusing detection efforts only on face regions rather than processing entire video frames. This selective approach applies detection resources partially to the most relevant areas (faces) while ignoring other parts of the video, achieving efficient monitoring without sacrificing identification accuracy.
Data Source
AI summary
A face detection method to be implemented by a camera and a server that are communicably connected to each other includes the steps of: acquiring a captured video captured by the camera; detecting a face of one or more persons based on the captured video; generating one or more face cut-out images by cutting out a region of the detected face; counting a number of detection times that a person who is the same as or similar to a person in the face cut-out image is detected in a predetermined detection period; determining a category of the person based on the number of detection times; and outputting the face cut-out image corresponding to the person, the number of detection times corresponding to the person, and the category corresponding to the person in association with each other.