Face Key Point Detection via Heat Map and Coordinate Fusion
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Solution Overview
Problem
Current face key point detection technologies face challenges in achieving high accuracy and real-time performance, with coordinate regression providing fast inference but low detection accuracy, and heat map regression offering high accuracy but low inference rates and reduced robustness.
Innovation Solution
An electronic device that simultaneously uses heat map and coordinate value features to detect face key points, employing a lightweight neural network-based heat map regression model and coordinate value regression model, and performs feature fusion through operations like element level multiplication, channel splicing, and pixel splicing to enhance detection accuracy and real-time performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If coordinate regression is used for face key point detection, then inference speed is fast, but detection accuracy is low
Solution Approach 1:
The patent merges coordinate regression and heat map regression into a unified dual-branch network architecture. The coordinate regression branch provides fast inference capability while the heat map regression branch provides high detection accuracy. The two branches are combined through feature fusion mechanisms (channel attention, spatial attention) to achieve both speed and accuracy simultaneously, resolving the contradiction between inference speed and detection accuracy.
Solution Approach 2:
The patent creates a composite detection approach by integrating two different regression methods (coordinate-based and heat-map-based) into a single system. Each method contributes its strengths: coordinate regression contributes computational efficiency while heat map regression contributes precision. The composite structure allows the system to leverage both fast inference and high accuracy.
2Measurement precision
If heat map regression is used for face key point detection, then detection accuracy is high, but inference rate is low and robustness is reduced
Solution Approach 1:
The patent combines heat map regression with coordinate regression in a dual-branch architecture. The heat map branch maintains its high accuracy advantage while the coordinate branch compensates for the low inference rate. Through feature fusion and attention mechanisms, the system achieves both high detection accuracy and improved inference rate, resolving the contradiction between accuracy and productivity.
Solution Approach 2:
The patent introduces dynamic feature fusion mechanisms including channel attention and spatial attention modules that adaptively weight the contributions of heat map features and coordinate features based on input characteristics. This dynamic adjustment allows the system to optimize the balance between accuracy and inference rate for different input scenarios, improving overall productivity while maintaining high detection accuracy.
3Measurement precision
If heat map regression is used for face key point detection, then detection accuracy is high, but robustness is reduced
Solution Approach 1:
The patent merges heat map regression with coordinate regression to create a more robust system. The coordinate regression component provides global positional information that complements the local precision of heat map regression. This combination makes the system more robust to variations in input quality, lighting conditions, and face orientations, while maintaining high detection accuracy through the heat map component.
Solution Approach 2:
The patent implements feedback mechanisms through attention modules that continuously adjust the weighting of heat map and coordinate features based on detection confidence and input characteristics. This feedback loop enhances robustness by adapting to different scenarios and compensating for weaknesses in either method, while preserving the high accuracy benefits of heat map regression.
4Measurement precision
If feature fusion is performed through multiple operations, then detection accuracy and real-time performance are enhanced, but computational complexity increases
Solution Approach 1:
The patent segments the feature fusion process into distinct functional modules: channel attention module, spatial attention module, and feature aggregation module. Each module performs a specific function in the fusion process, making the overall complex operation more manageable and efficient. This segmentation allows for optimized computation at each stage while achieving high detection accuracy through comprehensive feature integration.
Solution Approach 2:
The patent employs dynamic attention mechanisms that adaptively determine the importance of different features and fusion operations based on input characteristics. This dynamic approach allows the system to perform comprehensive feature fusion for high accuracy while avoiding unnecessary computational complexity in scenarios where full fusion is not needed, thus optimizing the balance between accuracy and computational efficiency.
Data Source
AI summary
An electronic device includes a memory configured to store instructions, and a processor configured to execute the instructions to configure the processor to obtain a first heat map feature and a first coordinate value feature based on a face image, and detect a face key point based on the first heat map feature and the first coordinate value feature.