Dual-Branch Neural Network Training for Low-Quality Face Recognition
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Solution Overview
Problem
Existing face recognition technologies struggle with low-quality face images, as they fail to effectively extract and utilize the attention and gradient maps from such images, leading to degraded performance.
Innovation Solution
A dual-branch neural network model is employed, where one branch processes high-quality images and the other low-quality images, utilizing a softened attention-based gradient texture-guided learning method to align the attention and gradient maps, thereby improving recognition performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a neural network is trained to recognize high-quality face images, then recognition accuracy for high-quality images is improved, but recognition performance for low-quality images deteriorates
Solution Approach 1:
The neural network is divided into two separate models: a first model trained on high-quality images and a second model trained on low-quality images. Each model specializes in processing images of its respective quality level, allowing both to achieve optimal performance without interference. The segmentation enables the system to handle different image quality scenarios with dedicated architectures.
Solution Approach 2:
An attention map serves as an intermediary between the first model and the second model. The attention map captures important features from high-quality images and guides the second model to focus on corresponding regions in low-quality images, effectively transferring useful information across quality boundaries without direct model coupling.
2Productivity
If face features are extracted from low-quality images, then processing speed is maintained, but feature quality deteriorates due to damaged face features
Solution Approach 1:
The system performs preliminary processing by generating an attention map from a high-quality image before processing the low-quality image. This attention map pre-identifies important facial regions and guides the subsequent feature extraction process, ensuring that even with damaged features in low-quality images, the most critical information is captured first.
Solution Approach 2:
The gradient map acts as an intermediary that bridges the quality gap. It captures the structural information from the low-quality image while being guided by the attention map, allowing the system to maintain processing speed while improving feature extraction quality through guided gradient computation.
3Ease of manufacture
If traditional training methods are used on low-quality images, then training simplicity is maintained, but recognition performance deteriorates
Solution Approach 1:
The attention map serves as a guiding intermediary during training. It is generated from high-quality images and used to weight the loss function computation for the second model, directing the training process to focus on important facial regions. This approach maintains relative training simplicity while significantly improving recognition performance on low-quality images.
Solution Approach 2:
The system introduces a weighted loss function that dynamically adjusts training parameters based on the attention map. By modifying the loss computation to emphasize important regions identified by the attention map, the training process adapts to prioritize learning from critical facial features, improving performance without fundamentally changing the training framework.
Data Source
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AI summary
A method and an apparatus for training a neural network, an image recognition method and a computer readable storage medium are disclosed. The neural network includes a first model and a second model. The method for training a neural network includes: acquiring a second image from a first image, wherein a quality of the second image is lower than that of the first image; inputting the first image into the first model of the neural network, and inputting the second image into the second model of the neural network; calculating an attention map and a gradient map of the first model and an attention map and a gradient map of the second model; constructing a loss function based on a matrix of a dot product of the gradient map and the attention map of the first model and a matrix of a dot product of the gradient map and the attention map of the second model; and training the neural network by minimizing the loss function.