Multi-Task Biometric Network With Landmark Mesh Pooling
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Solution Overview
Problem
Existing facial biometric algorithms face challenges with sparse or limited training datasets and redundant tasks across different applications, leading to inefficiencies in biometric analysis, particularly in systems like vehicles and traffic infrastructure.
Innovation Solution
A multi-task network architecture is employed, comprising a common feature extraction neural network and multiple biometric analysis task neural networks, utilizing facial landmarks for enhanced training and inference through a landmark mesh and expert pooling, which reduces processing runs and increases accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple separate neural networks are used for different biometric tasks, then each task can be optimized independently, but the system complexity increases and training efficiency decreases
Solution Approach 1:
The patent combines multiple task-specific neural networks into a single multi-task network that shares a common feature extraction backbone. This integration reduces system complexity while maintaining the ability to perform multiple biometric tasks (face detection, attribute analysis, recognition) through a unified architecture that processes images once and generates multiple outputs simultaneously.
Solution Approach 2:
The multi-task neural network is designed with universal functionality to perform multiple biometric analysis tasks concurrently. The shared feature extraction backbone serves all tasks, and the network can be configured to output results for different biometric functions based on the same input image, eliminating the need for separate dedicated networks for each task.
2Measurement precision
If multiple separate neural networks are trained independently, then each network can achieve task-specific optimization, but training time and computational resources increase
Solution Approach 1:
The patent merges multiple training processes into a single unified training procedure for the multi-task network. By sharing the common backbone and training all tasks simultaneously on the same dataset, the system reduces total training time and computational resource consumption compared to training separate networks independently, while still achieving task-specific optimization through task-specific output layers.
3Productivity
If a single neural network is used for multiple biometric tasks, then processing efficiency improves, but task-specific accuracy may decrease
Solution Approach 1:
The multi-task neural network is segmented into a shared feature extraction backbone and multiple task-specific output branches. This segmentation allows the common backbone to learn general facial features that benefit all tasks, while each task-specific branch can specialize in task-specific patterns and transformations, thereby maintaining high task-specific accuracy while achieving processing efficiency through unified input processing.
4Productivity
If limited training datasets are used, then data processing time is reduced, but model accuracy and generalization decrease
Solution Approach 1:
The multi-task network leverages its universal architecture to maximize the utility of limited training data. By training multiple tasks simultaneously on the same dataset, the system extracts more information from each training example, improving model accuracy and generalization without requiring additional data or increased training time, as the shared features benefit all tasks concurrently.
Data Source
AI summary
A selected biometric analysis task is performed in a deep neural network that includes a common feature extraction neural network, a plurality of task-specific neural networks, a segmentation neural network, a landmark mesh neural network, a plurality of soft target segmentation neural networks, and a plurality of expert pooling neural networks that perform the plurality of biometric analysis tasks by inputting the image to the common feature extraction network to determine latent variables. The latent variables can be input to the plurality of biometric task-specific neural networks to determine a plurality of first biometric analysis task outputs. The latent variables can be input to a landmark mesh neural network to determine a landmark mesh. The landmark mesh and the first biometric task outputs to a plurality of expert pooling neural networks to determine a plurality of second biometric task outputs.