Multi-Stage Neural Network Keypoint Detection
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Solution Overview
Problem
Conventional face keypoint detection systems 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 employed, where a first-stage neural network segments the image into regions, a second-stage neural network identifies keypoints within each region, and a third-stage neural network corrects keypoint locations, enabling efficient keypoint detection on resource-limited devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a conventional single-stage neural network is used for keypoint detection, then the system can detect keypoints directly from images, but the computational intensity increases geometrically with the number of keypoints, making it impractical for mobile devices and real-time operation
Solution Approach 1:
The patent divides the image into multiple regions of interest (ROIs) before processing, and further segments the keypoint detection task into multiple stages. The first stage identifies candidate regions, the second stage processes each region with a dedicated neural network, and the third stage refines results. This segmentation approach reduces the computational burden on each individual network component while maintaining overall detection accuracy.
Solution Approach 2:
The patent introduces a multi-dimensional processing approach by organizing keypoint detection across three temporal stages rather than processing all keypoints simultaneously in a single stage. This dimensional transformation from a single-layer to a multi-layer temporal structure allows computational tasks to be distributed and optimized at each stage, reducing the geometric growth of computational intensity.
2Adaptability or versatility
If the number of facial keypoints to be processed increases, then the detection coverage improves, but the systems become increasingly resource-hungry and impractical for mobile computing devices
Solution Approach 1:
The patent segments the image into multiple regions of interest and assigns different neural networks to different regions. This allows the system to process a large number of keypoints across the entire image while keeping each individual neural network relatively simple and resource-efficient. The segmentation strategy enables scalable keypoint detection that can handle increased keypoint counts without proportionally increasing resource consumption.
Solution Approach 2:
The patent applies the principle of local quality by using different neural networks optimized for specific regions of the image. Each regional neural network is tailored to detect keypoints in its specific region, allowing the system to handle diverse keypoint detection requirements across different image areas while maintaining efficiency. This local optimization enables the system to scale to more keypoints without uniformly increasing resource consumption across the entire system.
3Productivity
If a comprehensive single-stage neural network is used to detect all keypoints, then all keypoints can be detected in one pass, but the system is not useful for on-chip implementations for real-time operation
Solution Approach 1:
The patent segments the keypoint detection process into three distinct stages performed by different neural networks. The first stage identifies candidate regions, the second stage detects keypoints in each region, and the third stage refines the results. This segmentation enables real-time processing on resource-constrained chips by distributing the computational load across multiple simpler networks rather than requiring one large complex network to process all keypoints in a single pass.
Solution Approach 2:
The patent applies preliminary action by having the first neural network identify and segment candidate regions before the second neural network performs detailed keypoint detection. This preliminary processing step filters and organizes the input data, reducing the computational burden on subsequent stages and enabling real-time operation on chip-based systems. The preliminary segmentation allows the system to focus computational resources only on relevant regions, improving overall throughput.
Data Source
AI summary
Embodiments include systems and methods that may be performed by a processor of a computing device. Embodiments may be applied for keypoint detection in an image. In embodiments, the processor of the computing device may apply to an image a first-stage neural network to define and output a plurality of regions, apply to each of the plurality of regions a respective second-stage neural network to output a plurality of keypoints in each of the plurality of regions, and apply to the plurality of keypoints a third-stage neural network to determine a correction for each of the plurality of keypoints to provide corrected keypoints.