User-Specific Swipe Authentication for Continuous Device Access
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Solution Overview
Problem
Existing biometric authentication methods on user devices, such as facial recognition and fingerprint detection, are susceptible to unauthorized access and degrade user experience by requiring active user interaction, while gesture-based authentication can also be compromised or forgotten.
Innovation Solution
A user device authenticates users based on a learned or identified user-specific swipe, utilizing a machine learning model to analyze unique touch characteristics of a user's swipe motion, which is passive and resource-efficient, allowing continuous authentication during user sessions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If facial recognition or fingerprint detection is used for authentication, then security is improved, but user experience degrades due to requiring active user interaction and susceptibility to unauthorized access
Solution Approach 1:
The system performs authentication automatically without requiring active user participation. The machine learning model continuously monitors touchscreen interactions and autonomously determines user identity, eliminating the need for users to actively engage in authentication processes while maintaining security through passive behavioral analysis
Solution Approach 2:
The patent replaces physical biometric verification mechanisms (facial recognition cameras, fingerprint sensors) with a software-based machine learning system that analyzes touchscreen interaction patterns. This substitution eliminates hardware vulnerabilities and provides more reliable authentication through behavioral biometrics while requiring no additional user actions
2Ease of operation
If gesture-based authentication is used, then ease of operation is improved, but reliability degrades due to susceptibility to compromise and forgetting
Solution Approach 1:
The system automatically performs gesture recognition and authentication without requiring users to consciously execute specific gestures. The machine learning model passively analyzes natural touchscreen interactions and autonomously identifies user identity, combining the ease of natural gestures with the reliability of automated recognition
Solution Approach 2:
The patent transforms the authentication approach by changing from recognizing specific predefined gestures to analyzing continuous parameters of touchscreen interactions (touch pressure, swipe velocity, contact duration, gesture curvature). This parameter-based approach creates more reliable authentication by capturing subtle behavioral characteristics that are harder to compromise or forget
3Reliability
If continuous authentication monitoring is implemented, then reliability is improved, but computing resources are consumed
Solution Approach 1:
The system performs partial authentication analysis by monitoring only essential touchscreen interaction parameters rather than complete behavioral profiles. The machine learning model processes only the minimum necessary data (touch start/end coordinates, duration, pressure) to perform authentication, reducing computational overhead while maintaining continuous monitoring and reliability
Solution Approach 2:
The patent implements continuous authentication monitoring that operates seamlessly in the background during normal device usage. The machine learning model continuously analyzes touchscreen events without interrupting user workflows, providing uninterrupted security verification while optimizing resource usage through efficient event-driven processing rather than constant full-system analysis
Data Source
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AI summary
In some aspects, a device may receive swipe data associated with a swipe by a user of a user device. The swipe data may be received based on the swipe generating a user input that is indicative of a swipe input. The device may process, based on reference swipe data, the swipe data to determine whether the swipe is associated with an authorized user of the user device. The reference swipe data may be associated with previous swipes performed by the authorized user. The device may perform an action associated with access to a user interface of the user device based on whether the swipe data is determined to be associated with the authorized user. Numerous other aspects are described.