Electronic Camera Face Recognition Data Segmentation
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Solution Overview
Problem
Conventional electronic cameras with face recognition functions face challenges in easily generating face recognizing data, managing and editing registered data, and efficiently processing multiple faces, leading to increased computational time and user inconvenience.
Innovation Solution
The electronic camera is equipped with an image sensor, image processing section, face detecting section, controlling section, face image generating section, and recording section, which together facilitate easy face registration, data generation, and efficient processing by adjusting shooting parameters, displaying composition assistance, and prioritizing face recognition based on detected features and user intentions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If more face recognizing data are registered to improve recognition accuracy, then recognition accuracy is improved, but device complexity and difficulty of data management increase
Solution Approach 1:
The patent divides face recognizing data into multiple categories based on detected face features (e.g., expression, pose, lighting conditions). This segmentation allows the system to manage large amounts of data in an organized manner while improving recognition accuracy by selecting appropriate data subsets for different recognition scenarios.
Solution Approach 2:
The patent introduces a feature-based indexing system as an intermediary between the raw face images and the recognition process. This indexing mechanism categorizes and organizes face data according to extracted features, making data management more efficient and reducing the complexity of handling large datasets.
2Measurement precision
If face recognition processing is performed with large amounts of data to improve accuracy, then recognition accuracy is improved, but computational time increases
Solution Approach 1:
The patent applies partial action by selecting only the necessary face recognizing data based on detected face features and recognition requirements. Instead of processing all registered data, the system selectively uses relevant data subsets, reducing computational time while maintaining adequate recognition accuracy.
Solution Approach 2:
The patent performs preliminary feature extraction and data categorization during the registration phase. By pre-organizing face data according to features like expression and pose, the system reduces the computational burden during actual recognition operations, as it only needs to compare against relevant pre-categorized data.
3Adaptability or versatility
If multiple faces are detected in a shooting screen to improve recognition capability, then face recognition capability is improved, but computational requirements and processing time increase
Solution Approach 1:
The patent segments the face recognition process into independent modules for each detected face. Each face is processed separately through feature extraction and matching, allowing the system to handle multiple faces efficiently without requiring a single complex processing operation for all faces simultaneously.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This configuration enables easy face registration, quick data management, and optimized face recognition processing, allowing users to efficiently capture and recognize faces while reducing computational time and enhancing user experience.
Implementation Method 1
an image sensor, which photoelectrically converts a subject image obtained by a shooting optical system to generate an image signal
Data Source
AI summary
An image sensor of an electronic camera photoelectrically converts a subject image obtained by a shooting optical system to generate an image signal. A image processing section generates face registration image data and moving image data. A face detecting section detects a face area inside a shooting screen based on the moving image data. A controlling section adjusts shooting parameters of the shooting optical system, depending on a position detected at the face area. A face image generating section cuts out an image of the face area to generate face image data. A face recognizing data generating section extracts feature points of the face of a captured person from a part of the face area of the face registration image data and generates face recognizing data. A recording section records the face recognizing data or face image data.