Cascade Face Key Point Detection for Mobile Resource Constraints
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Solution Overview
Problem
Existing biometric processing technologies face challenges in accurately and efficiently detecting face key points on mobile devices with scarce resources, as larger network models and hierarchical models require significant computation and resources, making real-time detection difficult.
Innovation Solution
A method involving a cascade network structure with three-level processing, including a face key point detecting model, a posture angle estimating model, and a position correcting model, which distinguishes face types to simplify positioning and reduce computational load, ensuring accurate and real-time detection of face key points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a larger network model is introduced to improve detection accuracy, then the accuracy in detecting face key points is improved, but the computational load and resource consumption increase, making real-time detection difficult on mobile devices
Solution Approach 1:
The patent divides the face detection task into multiple stages using a cascade network structure. The first stage uses a simpler model for rough detection, and subsequent stages progressively refine the results with more complex models. This segmentation allows the system to achieve high accuracy while keeping the computational load of each individual stage manageable for mobile devices.
Solution Approach 2:
The patent employs adaptive model selection based on detected face types. Different face types (e.g., frontal, profile, angled) are identified and matched with appropriately complex detection models. This dynamic adaptation ensures that the system uses only the necessary computational resources for each specific case, rather than always deploying the most complex model.
2Measurement precision
If a more hierarchical network model is introduced to improve detection accuracy, then the accuracy in detecting face key points is improved, but the time complexity increases, making real-time detection difficult
Solution Approach 1:
The cascade network structure segments the detection process into multiple passes, where each pass processes only the regions of interest identified in previous passes. This reduces the overall time complexity compared to applying a single complex hierarchical model to the entire image, while still achieving high accuracy through progressive refinement.
Solution Approach 2:
The patent performs preliminary face type classification and rough key point detection before applying more complex correction models. This preliminary action narrows down the search space and reduces the computational time required for subsequent detailed detection, enabling real-time performance.
3Measurement precision
If a larger network model is introduced to improve detection accuracy, then the accuracy in detecting face key points is improved, but the demand on resources increases, making it difficult to apply to mobile terminals
Solution Approach 1:
By segmenting the detection task into multiple stages with progressively complex models, the patent reduces the resource demand of each individual model stage. The first stage uses a lightweight model that consumes minimal resources, while subsequent stages process only the refined regions, thereby distributing resource consumption across multiple lighter components rather than requiring one large resource-intensive model.
Solution Approach 2:
The patent applies full computational resources only when necessary. For example, complex correction models are applied selectively based on face type and detection confidence levels. In many cases, simpler models provide sufficient accuracy, allowing the system to conserve resources while maintaining high accuracy when needed.
Data Source
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AI summary
Provided are a face key point detection method, apparatus, computer device and storage medium, wherein the method comprises: obtaining image data containing a face; detecting a first face key point of the face in the image data; identifying a face type of the face according to the first face key point; performing position correction on the first face key point with reference to the face type; extracting facial features image containing facial features from the image data based on the first face key point after position correction; and detecting a second face key point of the face in the facial features image.