Behavioral Biometric Authentication via Swipe Gesture Analysis
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Solution Overview
Problem
Computing devices, such as smartphones, lack effective built-in security measures, making them vulnerable to unauthorized access, as traditional security methods like passwords, smart cards, and biometrics are either cumbersome or disrupt user experience.
Innovation Solution
The method involves analyzing and categorizing user interface input via touchpad or touchscreen devices to create behavioral biometric profiles based on statistical analysis of gestures, pressure, and motion, combining these profiles for authentication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional security methods (passwords, smart cards, biometrics) are implemented, then security is improved, but user experience is disrupted or operation becomes cumbersome
Solution Approach 1:
The system automatically captures and analyzes swipe gestures without requiring user setup or intervention. The behavioral biometric profile is created and maintained automatically through normal device usage, eliminating the need for users to manually configure security settings or carry additional security devices.
Solution Approach 2:
The patent replaces traditional mechanical security devices (smart cards, OTP tokens) and complex authentication systems with an automated behavioral analysis system that uses software-based gesture recognition and statistical profiling to provide security transparently.
2Reliability
If behavioral biometric analysis is implemented, then security is improved by distinguishing human from machine behavior, but system complexity increases
Solution Approach 1:
The swipe gesture analysis system serves multiple functions: it provides security authentication, creates behavioral biometric profiles for future recognition, and operates as a transparent layer over existing device functionality. The same gesture input is simultaneously used for device operation and security profiling.
Solution Approach 2:
The system transforms physical gesture parameters (position, velocity, acceleration, pressure, timing) into behavioral biometric data through statistical analysis. By changing the parameters from raw sensor data to normalized behavioral profiles, the system manages complexity while maintaining security effectiveness.
3Measurement precision
If statistical profiling of individual users is implemented, then authentication accuracy is improved, but data storage requirements increase
Solution Approach 1:
The system extracts only the essential behavioral parameters from complete gesture data, storing condensed statistical profiles rather than raw gesture sequences. By taking out and storing only the critical statistical features (timing patterns, velocity profiles, pressure distributions), the system maintains authentication accuracy while minimizing storage requirements.
Data Source
AI summary
Recording, analyzing and categorizing of user interface input via touchpad, touch screens or any device that can synthesize gestures from touch and pressure into input events. Such as, but not limited to, smart phones, touch pads and tablets. Humans may generate the input. The analysis of data may include statistical profiling of individual users as well as groups of users, the profiles can be stored in, but not limited to data containers such as files, secure storage, smart cards, databases, off device, in the cloud etc. A profile may be built from user/users behavior categorized into quantified types of behavior and/or gestures. The profile might be stored anonymized. The analysis may take place in real time or as post processing. Profiles can be compared against each other by all the types of quantified behaviors or by a select few.