Residual Deconvolution Network for Facial Image Analysis
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Solution Overview
Problem
Existing facial image analysis technologies face challenges in efficiently extracting high-dimensional feature maps from low-dimensional inputs using neural networks, particularly in effectively combining feature extraction and resolution enhancement for accurate analysis.
Innovation Solution
A method utilizing a residual network with sequentially combined residual blocks and a residual deconvolution network to process facial images, where the output of an N-th residual block is transferred to the deconvolution network, combined with prior information and enhanced using a dense condition random field method, to achieve improved analysis results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a deep neural network is used to extract features and reduce resolution using pooling layers, then low-dimensional feature abstraction is achieved, but it becomes difficult to efficiently transform low-dimensional feature maps back to high-dimensional pixel level for accurate semantic analysis
Solution Approach 1:
The network is divided into multiple stages with residual blocks at different depths, where each stage processes features at different resolutions. The deconvolution network is also segmented into multiple blocks that correspond to specific residual blocks, enabling staged reconstruction of high-dimensional feature maps from low-dimensional inputs.
Solution Approach 2:
The patent introduces intermediate deconvolution blocks that act as mediators between the residual network and the final analysis output. These deconvolution blocks progressively transform low-dimensional feature maps into high-dimensional pixel-level representations, bridging the gap between feature abstraction and pixel-level analysis.
2Measurement precision
If more residual blocks are added to improve feature extraction capability, then analysis accuracy improves, but model size and computational complexity increase
Solution Approach 1:
The patent extracts and utilizes intermediate feature maps from specific residual blocks (N-th residual block where N is less than the total number of residual blocks) without requiring the complete processing of all residual blocks. This allows the system to take out useful intermediate representations that are sufficient for the deconvolution network to generate accurate analysis maps, reducing unnecessary computational overhead.
Solution Approach 2:
Instead of using all residual blocks for deconvolution, the patent applies partial action by selecting only the N-th residual block output, where N is optimized to provide sufficient feature information. This partial utilization achieves effective feature extraction without the excessive complexity of processing all residual block outputs.
3Measurement precision
If conventional neural networks are used for facial image analysis, then basic feature extraction is achieved, but efficient combination of feature extraction and resolution enhancement for accurate pixel-level analysis is not realized
Solution Approach 1:
The patent merges the residual network for feature extraction with the deconvolution network for resolution enhancement into an integrated architecture. The deconvolution network is specifically designed to correspond to the residual blocks, creating a unified system that simultaneously performs feature extraction and high-dimensional reconstruction, achieving both accuracy and efficiency.
Solution Approach 2:
The residual network performs preliminary feature extraction and dimensionality reduction before the deconvolution network processes the intermediate features. This preliminary action prepares the data in an optimized form that enables the deconvolution network to efficiently reconstruct high-dimensional feature maps with accurate pixel-level information.
Data Source
AI summary
A method to analyze a facial image includes: inputting a facial image to a residual network including residual blocks that are sequentially combined and arranged in a direction from an input to an output; processing the facial image using the residual network; and acquiring an analysis map from an output of an N-th residual block among the residual blocks using a residual deconvolution network, wherein the residual network transfers the output of the N-th residual block to the residual deconvolution network, and N is a natural number that is less than a number of all of the residual blocks, and wherein the residual deconvolution network includes residual deconvolution blocks that are sequentially combined, and the residual deconvolution blocks correspond to respective residual blocks from a first residual block among the residual blocks to the N-th residual block.