Facial Key Point Positioning Using Multi-Model Segmentation
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Solution Overview
Problem
Existing face positioning technologies face challenges in accurately determining key points on faces due to variations in posture, facial expressions, lighting, gender, skin tone, and race, affecting the stability and validity of facial recognition.
Innovation Solution
A method that uses multiple positioning models trained on different types of facial image samples to acquire and evaluate key point positions, updating the models based on evaluation results to improve accuracy, involving the use of SIFT features and classifiers to select and refine positioning results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single positioning model is used for face key point detection, then the device complexity is low, but the positioning accuracy deteriorates due to variations in posture, facial expressions, lighting, gender, skin tone, and race
Solution Approach 1:
The patent divides the face positioning task into multiple specialized positioning models, each trained on specific facial characteristics (e.g., different races, genders, age groups). Instead of using one general model, the system segments the problem into multiple focused models that can be selected and applied based on the input image characteristics, thereby improving accuracy without requiring a single overly complex model to handle all variations.
Solution Approach 2:
The patent applies local quality by training different positioning models with specialized features tailored to specific facial characteristics. Each model is optimized for particular conditions (e.g., Asian faces, European faces, children, adults), allowing the system to apply the most appropriate model locally based on the detected facial attributes, thus improving positioning accuracy for diverse populations.
2Measurement precision
If multiple positioning models are used to improve positioning accuracy across different facial characteristics, then the positioning accuracy improves, but the device complexity increases
Solution Approach 1:
The patent performs preliminary classification of the input facial image to determine its characteristics (race, gender, age group) before selecting the appropriate positioning model. This preliminary action allows the system to prepare and apply the most suitable pre-trained model in advance, improving positioning accuracy while managing complexity through intelligent model selection rather than simultaneously maintaining all models active.
Solution Approach 2:
The patent changes the parameter of model selection based on detected facial characteristics. By dynamically selecting which positioning model to apply based on image analysis results (such as skin tone, facial structure, age indicators), the system adapts to different facial types, improving accuracy across diverse populations while keeping the overall system complexity manageable through parameter-based model selection.
3Adaptability or versatility
If positioning models are trained on diverse facial image samples to improve adaptability, then the adaptability improves, but the training time and data processing requirements increase
Solution Approach 1:
The patent segments the training process by creating separate training datasets for different facial characteristics (race, gender, age groups) and training individual positioning models for each segment. This approach allows parallel training of multiple specialized models rather than training one massive model on all data, improving adaptability to diverse facial characteristics while managing training time through distributed, specialized training processes.
Data Source
AI summary
Provided are a positioning method and apparatus. The positioning method includes acquiring a plurality of positioning results including positions of key points of a facial area included in an input image, respectively using a plurality of predetermined positioning models, evaluating the plurality of positioning results using an evaluation model of the positions of the key points, and updating at least one of the plurality of predetermined positioning models and the evaluation model based on a positioning result that is selected, based on a result of the evaluating, from among the plurality of positioning results.