Multifactor Authentication Framework for In-Air Handwriting
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Solution Overview
Problem
Existing user login systems for VR and mobile platforms face challenges in user identification due to the fuzziness and lack of distinctiveness in gesture-based authentication, making it difficult to efficiently index and search gesture patterns in a large account database.
Innovation Solution
A multifactor authentication framework that combines in-air-handwriting and hand geometry using a deep convolutional neural network to generate a compact binary hash code, allowing for efficient user identification and authentication by fusing secret, behavior, and physiological traits, and utilizing a hash table for fast database search.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If gesture-based authentication is used for user login, then ease of operation is improved, but measurement precision deteriorates due to inherent fuzziness in gestures
Solution Approach 1:
The patent combines multiple authentication factors including in-air handwriting gestures, hand geometry biometrics, and secret passcodes into a unified authentication framework. This merging of multiple modalities compensates for the fuzziness of individual gesture signals while maintaining ease of operation, as the system can tolerate variations in gesture execution while still achieving accurate user identification through the combined information from multiple sources.
2Ease of operation
If gesture patterns are used for user identification, then ease of operation is improved, but reliability deteriorates due to difficulty in providing sufficient information for large account ID space
Solution Approach 1:
The patent transitions from traditional 2D touchscreen interaction to 3D in-air gesture space, adding spatial dimensions to the authentication process. By capturing hand geometry, finger motion trajectories, and three-dimensional gesture patterns, the system creates a much larger feature space that can reliably distinguish between numerous accounts. This dimensional expansion provides sufficient information entropy to support large account ID spaces while maintaining gesture-based ease of operation.
3Device complexity
If traditional indexing methods are used for gesture patterns, then device complexity is reduced, but productivity deteriorates due to difficulty in enabling fast identification
Solution Approach 1:
The patent pre-processes and encodes gesture patterns into compact feature representations and hash codes during the authentication setup phase. By performing preliminary encoding of hand geometry measurements and gesture patterns into standardized formats with fixed-dimensional feature vectors, the system enables extremely fast retrieval and comparison operations during actual authentication. This preliminary action reduces the computational burden during real-time identification, dramatically improving productivity without requiring complex real-time processing infrastructure.
Data Source
AI summary
Various embodiments of a framework for user authentication based on finger motion signal and hand geometry matching are disclosed herein.