Smart Group Portrait System Using Facial Detection and Sub-Image Composition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Capturing a single image of a group where all participants exhibit desirable facial characteristics, such as facing the camera, eyes open, and smiling, is challenging, especially with children, as existing solutions are time-consuming, require manual labor, and may not always produce an acceptable image due to the need for multiple image captures and high memory usage.
Innovation Solution
A method and system that perform facial detection to identify individuals in a group, monitor their features, determine an image quality score, and capture sub-images only when the score meets threshold values, combining these sub-images to form a single composite image, thereby reducing the need for multiple image captures and manual processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple images are captured and stored to ensure all participants exhibit desirable facial characteristics, then the reliability of obtaining an acceptable group image is improved, but the memory space consumption and time required increase significantly
Solution Approach 1:
The patent divides the group image capture into individual sub-image captures for each participant. Instead of capturing multiple complete group images and then selecting the best ones, the system captures individual sub-images of each person's face and combines them to form the final group image. This segmentation approach ensures all participants are captured in their best moments while avoiding the need to store and process multiple complete group images, thus reducing memory consumption.
Solution Approach 2:
The system performs preliminary facial detection and feature monitoring for each participant before capturing sub-images. By pre-identifying individuals and monitoring their facial features (eye openness, smile, head orientation), the system determines the optimal timing for capturing each sub-image. This preliminary action ensures high-quality captures are obtained efficiently without requiring multiple redundant group image captures.
2Manufacturing precision
If multiple images are captured and processed in post processing to create a better group image, then the quality of the final image is improved, but the time consumption and manual labor requirements increase
Solution Approach 1:
The system automatically performs facial detection, feature monitoring, quality scoring, and sub-image capture for each participant without requiring manual intervention. The computer vision system autonomously identifies individuals, monitors their facial features in real-time, determines when to capture sub-images based on quality thresholds, and combines these sub-images into the final group image. This self-service automation eliminates manual labor and significantly reduces processing time compared to traditional post-processing methods.
Solution Approach 2:
The system continuously monitors facial features of all participants in real-time during the capture process. Instead of capturing discrete group images and then performing batch post-processing, the system maintains continuous monitoring and capture of individual sub-images as participants naturally pose and interact. This continuous action ensures optimal images are captured at the moment they occur, reducing overall processing time while maintaining high image quality.
3Manufacturing precision
If manual selection and combination of best images is performed, then the ease of operation is reduced, but the control over final image quality is improved
Solution Approach 1:
The system automatically performs all operations including facial detection, feature monitoring, quality assessment, sub-image capture, and image combination without requiring user intervention. Users simply point the camera at the group and press a capture button, while the system handles the complex tasks of identifying individuals, monitoring their facial features, selecting optimal moments for capture, and assembling the final image. This automation maintains precise control over image quality while maximizing ease of operation.
Solution Approach 2:
The system incorporates real-time feedback through quality scoring of each participant's facial features. As the system monitors features such as eye openness, smile, and head orientation, it calculates a quality score for each participant and uses this feedback to determine when to capture sub-images. This automated feedback loop ensures high image quality is maintained while operating simply for the user, as the system self-regulates the capture process based on real-time visual analysis.
4Productivity
If the system monitors and captures sub-images based on image quality scores, then the productivity of image capture is improved, but the device complexity increases
Solution Approach 1:
The system uses a single integrated computer vision platform that performs multiple functions: facial detection, feature monitoring, quality scoring, and sub-image capture. Rather than using separate specialized systems for each function, the patent implements a universal vision system that handles all tasks through coordinated modules. This multi-functional approach improves productivity by streamlining the capture process while managing device complexity through unified software architecture and shared computational resources.
Data Source
AI summary
Various systems and methods are provided for capturing an image of a group of individuals in a scene. Facial detection may be performed to identify one or more individuals of the group. One or more features of each identified individual are monitored to determine an image quality score for the individual, indicative of how the individual will appear in a captured image of the group. A determination is made as to whether the image quality score for the individual satisfies one or more image quality threshold values. If the image quality score for the individual satisfies the one or more threshold values, a sub-image may be captured of the individuals. Sub-images for each individual may be combined to form a single composite image of the group.