Face Detection for Digital Image Rendering Parameter Adjustment
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Solution Overview
Problem
Conventional digital image processing techniques fail to effectively detect and enhance images containing faces, especially in complex backgrounds or with multiple faces, and do not utilize face detection information for automatic image improvement.
Innovation Solution
A method within a digital rendering device that uses face detection to adjust rendering parameters such as exposure, orientation, color balance, and compression, by identifying face pixels and comparing default image attributes with rendered image attributes to adjust rendering parameters accordingly, allowing for automatic image enhancement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional digital image processing techniques are used, then general image processing is performed, but face detection and enhancement is not achieved
Solution Approach 1:
The image processing is segmented into general processing and face-specific processing. Face detection algorithms identify facial regions, and these regions are then processed separately with face-specific enhancement techniques, allowing both general and specialized processing to occur efficiently
Solution Approach 2:
Face detection is performed as a preliminary step before enhancement. The system first identifies face locations and characteristics, then uses this information to guide subsequent enhancement operations, ensuring that enhancement is applied precisely where needed
2Manufacturing precision
If face detection is implemented to improve image enhancement, then processing time and computational complexity increase
Solution Approach 1:
The system applies face-specific enhancement only to detected facial regions rather than processing the entire image uniformly. This partial action approach maintains high rendering quality for faces while reducing overall processing time and computational resources required
Solution Approach 2:
Different processing qualities are applied to different regions: face regions receive specialized high-quality enhancement, while non-face regions receive standard processing. This local quality approach optimizes the balance between rendering quality and processing efficiency
3Measurement precision
If multiple faces are detected and processed individually, then processing complexity increases, but if processed as a whole, then individual face quality deteriorates
Solution Approach 1:
Multiple faces are detected and segmented as separate regions of interest. Each face is then processed individually with face-specific algorithms, allowing precise attribute measurement for each face while managing complexity through modular processing of separate face instances
Solution Approach 2:
A universal face processing framework is implemented that can handle any number of faces through the same detection and enhancement pipeline. The system uses consistent algorithms for detecting, analyzing, and enhancing multiple faces, reducing overall system complexity while maintaining individual face quality
Data Source
AI summary
Within a digital rendering device, such as printers, hard copy and soft copy display, and copiers, rendering parameters of a digital image are perfected as part of an image rendering process using face detection within said rendered image to achieve one or more desired image rendering parameters. Default values are determined of one or more image attributes of at least some portion of the digital image. Values of one or more digital-rendering-device rendering parameters are determined. Groups of pixels are identified that correspond to an image of a face within the digitally-rendered image. Corresponding image attributes to the groups of pixels are determined. One or more default image attribute values are compared with one or more rendered image attribute values based upon analysis of the image of the face. One or more digital-rendering-device rendering parameters are then adjusted corresponding to adjusting the image attribute values.