Hierarchical Interlinked Multiscale Network for Face Parsing
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Solution Overview
Problem
Image parsing, particularly for face images, is challenging due to poorly defined landmark points and the deformability of face parts, and existing methods struggle to effectively integrate multiscale information for accurate parsing.
Innovation Solution
A hierarchical interlinked multiscale convolutional neural network (HIM) framework is employed to generate and integrate image patches at different scales, enabling better utilization of data across various resolutions for locating and segmenting image components, including a level generator, interlinked combiner, and aggregator to refine component locations and footprints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If landmark extraction is used to locate components, then component locations can be obtained, but the landmark points are not well-defined and uncertainty cannot be encoded
Solution Approach 1:
The patent segments the image into multiple scales (coarse to fine) and processes each scale separately through the cascaded hierarchical model. Instead of relying on single-scale landmark extraction, the system divides the parsing task across multiple resolution levels, where each level contributes to the final component localization and uncertainty estimation.
Solution Approach 2:
The patent adds a scale dimension to the image parsing problem by processing images at multiple resolutions. The cascaded hierarchical model operates across this scale dimension, allowing the system to capture both global context (coarse scales) and local details (fine scales), thereby improving landmark definition and uncertainty encoding.
2Device complexity
If single-scale information is used for image parsing, then processing is simpler, but accurate parsing cannot be achieved due to inability to capture both global characteristics and local contours
Solution Approach 1:
The patent segments the image processing task across multiple scales, with each scale handling specific aspects of component detection. Coarse scales capture global characteristics while fine scales capture local contours, and the cascaded hierarchical model integrates these segmented scale-specific features to achieve accurate parsing.
Solution Approach 2:
The patent introduces a scale dimension by processing images at multiple resolutions. This dimensional expansion allows the system to simultaneously capture global characteristics (at coarse scales) and local contours (at fine scales), resolving the trade-off between processing complexity and parsing accuracy.
3Ease of operation
If face parts are treated as rigid structures, then localization is easier, but accurate segmentation fails due to deformability of face parts
Solution Approach 1:
The patent treats face parts as dynamic and deformable structures rather than rigid bodies. The cascaded hierarchical model at multiple scales can adapt to the deformable nature of face parts, capturing their varying shapes and configurations across different scales while maintaining accurate segmentation.
Data Source
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AI summary
A disclosed facial recognition system (and method) includes face parsing. In one approach, the face parsing is based on hierarchical interlinked multiscale convolutional neural network (HIM) to identify locations and/or footprints of components of a face image. The HIM generates multiple levels of image patches from different resolution images of the face image, where image patches for different levels have different resolutions. Moreover, the HIM integrates the image patches for different levels to generate interlinked image patches for different levels, where interlinked image patches for different levels have different resolutions. Furthermore, the HIM combines the interlinked image patches to identify refined locations and/or footprints of components.