3D Biometric Enrollment for Alignment-Tolerant Authentication
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Solution Overview
Problem
Existing biometric authentication methods, such as facial recognition, often require precise alignment and fail to utilize three-dimensional characteristics, leading to false negatives and unnecessary iterations, wasting time and energy, particularly in battery-operated devices.
Innovation Solution
Implementing methods and interfaces that enhance biometric authentication by reducing the need for precise alignment and utilizing three-dimensional features, providing faster and more efficient authentication processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If precise alignment is required during biometric enrollment and authentication, then authentication accuracy is improved, but user convenience deteriorates and false negatives increase
Solution Approach 1:
The patent transitions from two-dimensional image-based biometric analysis to three-dimensional depth map analysis. By capturing depth information using a depth sensor (e.g., time-of-flight camera or structured light sensor), the system creates a 3D representation of the user's face or other biometric feature. This additional dimensional data enables more robust alignment tolerance and accurate authentication even when the user's position or orientation varies slightly, thereby improving ease of operation while maintaining authentication accuracy.
2Reliability
If multiple iterations of biometric authentication are performed, then authentication reliability is improved, but time consumption increases
Solution Approach 1:
The system performs preliminary actions by capturing comprehensive three-dimensional biometric data during the enrollment phase, creating a detailed depth map and extracting multiple authentication features in advance. This preliminary data capture and feature extraction enable faster comparison and verification during subsequent authentication iterations, reducing the time required for each authentication attempt while maintaining high reliability through the richness of the pre-captured 3D data.
3Reliability
If multiple iterations of biometric authentication are performed, then authentication reliability is improved, but energy consumption increases
Solution Approach 1:
The system performs preliminary actions by capturing comprehensive three-dimensional biometric data during the enrollment phase, creating a detailed depth map and extracting multiple authentication features in advance. This preliminary data capture and feature extraction enable faster comparison and verification during subsequent authentication iterations, reducing the time required for each authentication attempt while maintaining high reliability through the richness of the pre-captured 3D data.
Solution Approach 2:
The patent replaces traditional mechanical or computational imaging methods with optical time-of-flight or structured light-based depth sensing. This substitution enables faster data acquisition and processing, reducing the computational burden and energy consumption associated with multiple authentication iterations. The 3D depth maps provide sufficient authentication information in a single capture, minimizing the need for repeated sensing and processing cycles.
4Device complexity
If two-dimensional representation is used for biometric feature analysis, then device complexity is reduced, but authentication accuracy deteriorates due to inability to analyze three-dimensional characteristics
Solution Approach 1:
The patent transitions from two-dimensional image-based biometric analysis to three-dimensional depth map analysis. By capturing depth information using a depth sensor (e.g., time-of-flight camera or structured light sensor), the system creates a 3D representation of the user's face or other biometric feature. This additional dimensional data enables more robust alignment tolerance and accurate authentication even when the user's position or orientation varies slightly, thereby improving ease of operation while maintaining authentication accuracy.
Data Source
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AI summary
The present disclosure relates generally to implementing biometric authentication. In some examples, a device provides user interfaces for a biometric enrollment process tutorial. In some examples, a device provides user interfaces for aligning a biometric feature for enrollment. In some examples, a device provides user interfaces for enrolling a biometric feature. In some examples, a device provides user interfaces for providing hints during a biometric enrollment process. In some examples, a device provides user interfaces for application-based biometric authentication. In some examples, a device provides user interfaces for autofilling biometrically secured fields. In some examples, a device provides user interfaces for unlocking a device using biometric authentication. In some examples, a device provides user interfaces for retrying biometric authentication. In some examples, a device provides user interfaces for managing transfers using biometric authentication. In some examples, a device provides interstitial user interfaces during biometric authentication. In some examples, a device provides user interfaces for preventing retrying biometric authentication. In some examples, a device provides user interfaces for cached biometric authentication. In some examples, a device provides user interfaces for autofilling fillable fields based on visibility criteria. In some examples, a device provides user interfaces for automatic log-in using biometric authentication.