Vision-Based Hand Gesture Customization via Meta-Learning
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Solution Overview
Problem
Existing hand gesture recognition technologies face challenges in supporting user-defined and personalized gestures, particularly in learning from limited samples, which limits their applicability across diverse domains and for individuals with specific needs.
Innovation Solution
The proposed solution involves a comprehensive framework for gesture customization based on meta-learning, utilizing graph transformers, transfer learning, and meta-augmentation techniques to enable efficient learning from minimal user samples and accommodate a wide range of gestures, including static, dynamic, single-handed, and two-handed gestures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional hand gesture recognition technologies are used, then basic gesture detection is achieved, but the ability to learn from limited user samples and support personalized gestures is insufficient
Solution Approach 1:
The system performs preliminary actions by pre-training the model on comprehensive gesture datasets before deployment. This pre-training equips the model with foundational gesture understanding, enabling it to adapt to user-specific gestures with minimal samples while maintaining recognition accuracy
Solution Approach 2:
The system changes parameters by dynamically adjusting model confidence thresholds and data augmentation strategies based on the number of available user samples. This allows the system to optimize between adaptability and precision depending on the training data availability
2Adaptability or versatility
If comprehensive gesture customization is enabled, then user personalization and inclusivity are enhanced, but system complexity increases
Solution Approach 1:
The system segments the gesture recognition framework into distinct modules: a pre-trained base model for general gesture understanding, a meta-learning component for user adaptation, and a data augmentation module. This segmentation allows comprehensive customization capability while managing system complexity through modular design
Solution Approach 2:
The system achieves universality by designing a unified meta-learning framework that handles multiple gesture types (static, dynamic, single-handed, two-handed) and various user needs through a single adaptable model, rather than requiring separate systems for each gesture category
3Loss of time
If minimal user samples are used for training, then data collection time is reduced, but the quality and reliability of gesture recognition may deteriorate
Solution Approach 1:
The system creates synthetic copies of user gestures through meta-augmentation techniques, generating additional training samples from the limited user-provided data. This copying approach maintains recognition reliability by providing sufficient training data without requiring extended data collection time from users
Data Source
AI summary
The subject technology provides vision-based hand gesture customization. An apparatus receives input data having image representations of a first hand gestures corresponding to different types of gestures. For each of the first hand gestures, the apparatus extracts features from one of the image representations of the first hand gestures to produce a skeleton-based feature representation of a corresponding hand gesture. The apparatus produces a first trained machine learning model by training a neural network with a first dataset having skeleton-based feature representations associated with the first hand gestures to predict a classification for the first hand gestures. The apparatus generates a second dataset having training samples representing at least a second hand gesture customized by a user. The apparatus produces a second trained machine learning model by training the first trained machine learning model based on the second dataset to predict a classification for the second hand gesture.