Multi-Face Image Processing With Clustered Two-Stage Detection
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Solution Overview
Problem
Existing image processing systems struggle to efficiently detect and discriminate facial expressions in images containing multiple faces, requiring significant computational resources and failing to achieve sufficient detection speed.
Innovation Solution
An image data processing device that employs a two-stage facial detection process using a first discrimination model for coarse detection followed by a second discrimination model for accurate detection within clustered areas, each tailored to specific facial attributes, and generates heat maps for expression analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single discrimination model is used to detect faces in images containing multiple faces, then the detection process is simple, but the detection speed is insufficient and computational resources are excessively consumed
Solution Approach 1:
The patent divides the image processing into multiple stages: first discrimination model for initial face detection, clustering process to group detected faces, and second discrimination model for detailed attribute analysis. This segmentation allows efficient processing by handling different tasks with specialized models rather than using a single complex model for all operations.
Solution Approach 2:
The first discrimination model performs preliminary face detection before the more resource-intensive second discrimination model is applied. By pre-identifying face locations and clustering them, the system reduces the computational burden on subsequent processing stages, improving overall detection speed while maintaining accuracy.
2Measurement precision
If a first discrimination model is used for initial face detection, then processing speed is improved, but detection accuracy for specific facial attributes is insufficient
Solution Approach 1:
The patent applies different discrimination models with specialized qualities to different processing stages. The first discrimination model is optimized for speed and general face detection, while the second discrimination model is optimized for specific facial attribute accuracy. This local quality assignment ensures each model performs its specific function at the appropriate level of precision.
Solution Approach 2:
The clustering process acts as an intermediary between the first and second discrimination models. It takes the output from the first model and prepares it for more detailed analysis by the second model, enabling a smooth transition from speed-optimized detection to accuracy-optimized attribute discrimination without direct conflict between the two requirements.
3Reliability
If comprehensive face detection is performed on the entire image, then all faces are detected, but computational resources are excessively consumed
Solution Approach 1:
The patent segments the comprehensive detection task into two phases: initial detection using the first discrimination model to identify all face locations, followed by selective detailed analysis using the second discrimination model only on clustered face groups. This segmentation maintains detection completeness while reducing overall computational resource consumption by avoiding exhaustive processing of the entire image at high detail levels.
Data Source
AI summary
An image data processing device and a system capable of efficiently detecting a face from an image including a plurality of faces are provided. A processor executes at least following processes including detecting a face of a person from an image represented by image data by using a first discrimination model and discriminating an attribute of the detected face, clustering an area where the face is detected in the image represented by the image data according to a type of the attribute of the face, selecting a second discrimination model to be applied to the area based on information on the type of the attribute of the face in the clustered area, and detecting a face of a person from an image of each area by using the second discrimination model applied to the area and discriminating an attribute of the detected face.