Face Image Processing via Feature Point Segmentation
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Solution Overview
Problem
Conventional human face image processing methods result in low similarity between processed images and users, with complex processing and lack of real-time display capabilities.
Innovation Solution
A method and apparatus for human face image processing that locates facial feature points, transforms facial images based on face shape and complexion distribution, and combines them to achieve a new human face image with higher similarity and real-time performance, using modules for location, extraction, shape transformation, complexion transformation, and image fusion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional picture-based face processing is used, then processing complexity is reduced, but image similarity to user and artistic quality deteriorate
Solution Approach 1:
The patent segments the face image processing into distinct modules: facial feature point detection, face shape extraction, complexion distribution analysis, and separate transformation operations. This modular segmentation enables precise control over each processing aspect, achieving high image similarity while managing complexity through structured organization of processing steps.
Solution Approach 2:
The patent performs preliminary extraction of face shape and complexion distribution characteristics before the actual transformation process. By pre-processing and storing these key features, the system prepares transformation data in advance, which reduces real-time processing complexity while maintaining high output quality and user similarity.
2Manufacturing precision
If complex processing operations are applied, then image quality and similarity improve, but real-time performance deteriorates
Solution Approach 1:
The system extracts and stores face shape and complexion distribution features in advance before transformation is needed. This preliminary action prepares all necessary transformation data beforehand, enabling rapid real-time application of transformations without compromising image quality or requiring complex on-the-fly calculations.
Solution Approach 2:
The patent creates simplified representations (copies) of the target face characteristics through feature point detection and parameter extraction. These copied features serve as transformation templates that can be efficiently applied multiple times without requiring full re-processing of the original complex images, thus enabling real-time performance.
3Manufacturing precision
If detailed facial feature transformation is implemented, then user similarity increases, but calculation complexity increases
Solution Approach 1:
The patent replaces complex mechanical image manipulation with mathematical parameter transformations. By representing facial features through coordinate points, shape parameters, and complexion distributions, the system uses computational mathematics instead of complex image processing operations, achieving high user similarity while reducing calculation complexity.
Solution Approach 2:
The system transforms detailed facial features by changing key parameters such as feature point coordinates, face shape descriptors, and complexion distribution values. This parameter-based approach allows precise control over transformation details for high user similarity while avoiding the need for complex pixel-level operations, thereby reducing overall calculation complexity.
Data Source
AI summary
The present disclosure discloses a method and apparatus for human face image processing. A specific embodiment of the method comprises: locating facial feature points in a human face image, extracting an image of a human face region according to a range defined by the facial feature points, transforming a facial image of the source image according to a face shape of the target image, transforming the facial image of the source image according to a complexion distribution of a facial region of the target image, and obtaining a new human face by combining the facial image of the source image and a facial image of the target image. The embodiment achieves a facial image processing with higher similarity to the user in the image, of simple steps, small calculation and high real-time performance.