Cascade Regression Trees for Robust Face Alignment
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Solution Overview
Problem
Existing face alignment approaches are computationally intensive and costly, often resulting in large prediction models that are difficult to store or update on mobile devices, and may not provide accurate shape predictions due to global training without considering variations in facial pose, lighting, and occlusion, leading to over-fitting issues.
Innovation Solution
A model using a cascade of regression trees with ordered, sequential levels, where each level consists of decision trees that evaluate and adjust the facial shape based on pixel values, utilizing barycentric coordinates to quickly determine pixel locations and reduce computational complexity, and employing a cost function to manage sensitivity to errors and outliers, with training data augmentation and dynamic learning factors to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a cascade of gradient boosted decision trees or Gaussian process regression trees is used for face alignment, then facial anchor points can be localized, but computational cost and power consumption increase significantly
Solution Approach 1:
The face alignment problem is segmented into multiple cascade levels, where each level processes the shape prediction with increasing complexity. Early levels use simpler models for coarse alignment, while later levels refine the prediction. This segmentation allows the system to achieve high accuracy without requiring all levels to use computationally intensive models simultaneously, thereby reducing overall computational cost and power consumption.
Solution Approach 2:
The system dynamically adjusts the complexity of processing at different cascade levels. Earlier levels in the cascade use less computationally intensive operations, while later levels apply more sophisticated adjustments only when needed. This dynamic approach ensures that computational resources are allocated efficiently, maintaining accuracy while minimizing energy consumption on mobile devices.
2Ease of manufacture
If prediction models are trained globally without considering variations in facial pose, lighting, and occlusion, then training is simpler, but prediction accuracy decreases due to over-fitting
Solution Approach 1:
The training process incorporates local quality variations by considering different facial conditions (pose, lighting, occlusion) at specific regions of the face. Each cascade level learns to handle specific variations locally rather than applying a single global model. This allows the system to maintain training feasibility while significantly improving prediction accuracy for diverse facial conditions.
Solution Approach 2:
The system changes parameters dynamically during training and prediction by adjusting learning rates and model complexity based on the specific facial variations detected. Different cascade levels use different parameter settings optimized for their specific processing stage, allowing the model to adapt to various facial conditions without requiring completely separate training processes for each scenario.
3Reliability
If a global learning factor is applied to all levels of the cascade to reduce over-fitting, then over-fitting is reduced, but shape prediction accuracy decreases
Solution Approach 1:
Instead of applying a uniform global learning factor, the system applies different learning factors at different cascade levels based on their specific needs. Earlier levels use learning factors optimized for coarse alignment, while later levels use different factors for refinement. This local optimization allows each level to achieve the best balance between over-fitting reduction and prediction accuracy for its specific function.
Solution Approach 2:
The learning factor is made dynamic rather than static, changing at each cascade level according to the processing stage. This dynamic adjustment allows the system to reduce over-fitting where necessary while maintaining or improving prediction accuracy at each level, resolving the contradiction between reliability and precision that arises with a fixed global learning factor.
4Measurement precision
If transformation matrix calculation is performed between current coordinates and default shape coordinates for each facial anchor point, then accurate positioning is achieved, but computational complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-computing and storing transformation relationships in the trained model structure. During runtime, instead of calculating transformation matrices from scratch for each anchor point, the system uses pre-learned transformation patterns from the cascade levels. This preliminary preparation significantly reduces computational complexity during actual face alignment while maintaining positioning accuracy.
Solution Approach 2:
The system uses copying by reusing the transformation relationships learned at earlier cascade levels for later levels. Instead of independently calculating transformations for each level, the system copies and adapts the transformation patterns from previous levels, reducing the overall computational complexity while maintaining the accuracy needed for precise facial anchor point positioning.
Data Source
AI summary
A face tracking system generates a model for extracting a set of facial anchor points on a face within a portion of a face image based a multiple-level cascade of decision trees. The face tracking system identifies a mesh shape adjusted to an image of a face. For each decision tree, the face tracking system identifies an adjustment vector for the mesh shape relative to the image of the face. For each cascade level, the face tracking system combines the identified adjustment for each decision tree to determine a combined adjustment vector for the cascade level. The face tracking system modifies adjustment of the mesh shape to the face in the image based on the combined adjustment vector. The face tracking system reduces the model to a dictionary and atom weights using a learned dictionary. The model may be more easily transmitted to devices and stored on devices.