Multi-Stage Neural Network Keypoint Detection
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Solution Overview
Problem
Conventional face keypoint detection methods are computationally intensive and resource-hungry, making them impractical for mobile computing devices and real-time on-chip implementations, especially as the number of keypoints increases.
Innovation Solution
A multi-stage neural network process is applied to an image, where a first neural network segments the image into regions, a second neural network identifies keypoints in each region, and a third neural network corrects keypoint locations, enabling efficient keypoint detection on resource-limited devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional face keypoint detection systems process an increasing number of facial keypoints, then the detection accuracy and completeness improve, but the computational resource requirements increase geometrically, making the system impractical for mobile devices and real-time implementations
Solution Approach 1:
The patent divides the face image into multiple regions (e.g., left eye region, right eye region, nose region, mouth region) and applies separate neural networks to each region. This segmentation allows the system to detect multiple keypoints simultaneously without requiring a single complex network to process the entire face, thereby reducing computational complexity while maintaining detection accuracy across all keypoints.
2Device complexity
If conventional systems use a single neural network to detect all keypoints directly from the image, then the system structure is simple, but the computational intensity increases geometrically with the number of keypoints
Solution Approach 1:
Instead of using one neural network for the entire face, the patent segments the face into multiple regions and applies separate neural networks to each region. This approach distributes the computational workload across multiple simpler networks, reducing the computational intensity of each individual network while maintaining the ability to detect all keypoints.
Solution Approach 2:
The patent extracts and processes specific regions of interest (eye regions, nose region, mouth region) separately from the rest of the face. By focusing computational resources on extracting and processing only the relevant regions containing keypoints, the system reduces overall computational intensity compared to processing the entire face image with a single network.
3Ease of operation
If conventional keypoint detection methods are implemented on mobile computing devices, then portability and accessibility improve, but the high computational requirements make real-time operation infeasible
Solution Approach 1:
The patent segments the face detection task into multiple region-specific neural networks that can be executed in parallel on mobile devices. This segmentation enables real-time processing by distributing computational workload across multiple simpler networks, making the system feasible for mobile computing devices with limited computational resources while maintaining real-time operation capability.
4Quantity of substance
If the number of facial keypoints to be processed increases, then the comprehensiveness of face analysis improves, but the system becomes increasingly resource-hungry and impractical for on-chip implementations
Solution Approach 1:
The patent segments the face into multiple regions and applies separate neural networks to detect keypoints in each region. This segmentation allows the system to process a large number of keypoints across the entire face by distributing the detection task across multiple region-specific networks, thereby reducing the resource consumption of each individual network while maintaining comprehensive keypoint detection.
Solution Approach 2:
The patent introduces a spatial dimension by dividing the face into multiple regions and processing them separately. This dimensional approach allows the system to handle a larger quantity of keypoints by organizing the detection task across different spatial regions, reducing the computational burden on each processing unit while maintaining the ability to detect all keypoints.
Data Source
AI summary
Embodiments include systems and methods for keypoint detection in an image. In embodiments, a processor of a computing device may apply to an image a first neural network that has been trained to define and output a plurality of regions. The processor may apply to each of the plurality of regions a respective second neural network to that has been trained to output a plurality of keypoints in each of the plurality of regions. The processor may apply to the plurality of keypoints a third neural network that has been trained to determine a correction for each of the plurality of keypoints to provide corrected keypoints suitable for the execution of an image processing function.