Facial Texture Target Model Training With Attention Gates
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Solution Overview
Problem
Conventional methods for identifying facial textures, such as under-eye bags, tear stains, and wrinkles, are cumbersome due to manual filter tuning and require high-precision hardware, making them inefficient and costly.
Innovation Solution
A neural network model with an encoder and decoder is trained using feature extraction, transformation, and upsampling blocks, along with attention gates to enhance accuracy and efficiency in identifying specific facial textures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If feature extraction based on texture using manual filters is used, then facial texture identification can be performed, but the implementation becomes complicated due to repeated parameter tuning
Solution Approach 1:
The neural network model automatically learns optimal filter parameters and features from training data without requiring manual intervention. The system performs self-service by autonomously optimizing its own configuration through backpropagation and gradient descent, eliminating the need for repeated manual parameter tuning while maintaining high identification accuracy
Solution Approach 2:
The patent replaces manual mechanical tuning of filter parameters with an automated computational system. The neural network uses algorithms to automatically adjust weights and biases during training, substituting the manual mechanical adjustment process with an automated computational optimization process that achieves the same goal more efficiently
2Productivity
If edge-detection operators are used for feature extraction, then processing can be performed, but the approach is inappropriate for large or wide facial textures since it relies on grayscale differences rather than actual texture locations
Solution Approach 1:
The patent transitions from two-dimensional grayscale intensity analysis to three-dimensional spatial feature representation by using convolutional neural networks that capture spatial relationships, textures, and patterns across multiple scales. This dimensional enhancement allows the system to accurately locate and characterize large or wide facial textures that edge-detection operators cannot properly identify
3Measurement precision
If three-dimensional scanners with great precision are used, then facial texture identification accuracy is improved, but hardware cost becomes high
Solution Approach 1:
The patent creates a computational model that copies and simulates the functionality of expensive three-dimensional scanning systems using standard two-dimensional image inputs. The neural network learns to infer three-dimensional facial texture characteristics from two-dimensional images, providing a cost-effective alternative that replicates the accuracy of high-precision hardware without requiring specialized scanning equipment
Solution Approach 2:
The patent replaces expensive, complex three-dimensional scanning hardware with inexpensive software-based neural network processing that can be deployed on standard computing devices. This substitution uses affordable computational resources instead of costly physical equipment, making the technology accessible and scalable without requiring high-precision hardware investments
Data Source
AI summary
A method includes: A) performing feature extraction on training images to obtain post-extraction images; B) performing transformation on the post-extraction images to obtain post-transformation images; C) performing feature identification on the post-transformation images to obtain post-identification images, each of the post-identification images having an identified mark indicating a specific type of facial texture; D) performing evaluation based on the post-identification images and ground truth images to obtain a loss value; E) determining whether the loss value is less than a preset threshold; F) in response to determining that the loss value is not less than the preset threshold, adjusting parameters of a neural network model, and repeating steps A) to E) by using the neural network model, the parameters of which have been adjusted; and G) in response to determining that the loss value is less than the preset threshold, designating the neural network model as a target model.