Drone Face Biometric Authentication Without Enrollment
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Solution Overview
Problem
Current authentication methods for drone delivery are inadequate as they require human-drone contact, are vulnerable to attacks like 3D-printed masks and adversarial examples, and lack mutual authentication, making them insecure and unusable for drone-user interaction.
Innovation Solution
The Smile2Auth system uses face biometrics by encoding frames from drone and user videos into embedding sequences, comparing them to authenticate both the drone and user without prior biometric data enrollment, and is resilient to attacks through real-time and unique facial gesture analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional face recognition is used for drone authentication, then authentication can be performed without physical contact, but it requires users to enroll their face information and is vulnerable to attacks like 3D-printed masks and adversarial examples
Solution Approach 1:
The system transitions from static face recognition to dynamic facial expression analysis. Instead of comparing static facial images against enrolled templates, the system captures video clips of users making facial expressions (e.g., smiling, raising eyebrows) and compares the temporal dynamics of these expressions. This dynamic approach makes it resistant to static spoofing attacks like 3D-printed masks and adversarial examples, as these cannot replicate the temporal variations and natural movements of genuine facial expressions.
Solution Approach 2:
The system performs preliminary actions by capturing and analyzing multiple facial expressions before final authentication. Users are prompted to perform specific facial actions (smiling, raising eyebrows) during the authentication process. The system records these preliminary facial movements and uses them as the basis for authentication, eliminating the need for prior enrollment while establishing a baseline for comparing genuine user identities.
2Measurement precision
If face recognition requires biometric enrollment, then authentication accuracy can be maintained, but user convenience and privacy are reduced due to data collection requirements
Solution Approach 1:
The system enables self-service authentication where the drone and user device independently perform the authentication process without requiring prior biometric enrollment or user intervention in data collection. The drone captures video, extracts facial embeddings, and compares them against the user's live facial expressions in real-time. This self-service approach eliminates the need for centralized biometric databases and manual enrollment processes, improving user convenience while maintaining accuracy through direct comparison of facial dynamics.
Solution Approach 2:
The system replaces the mechanical/enrollment-based face recognition system with a direct comparison system. Instead of using stored biometric templates and complex enrollment procedures, the system uses a simplified approach where the drone's captured facial embeddings are directly compared against live video embeddings in real-time. This substitution eliminates the need for biometric databases and enrollment processes, reducing system complexity and improving user convenience.
3Device complexity
If only one-way authentication is used, then the system complexity is reduced, but security is compromised as mutual authentication between drone and user cannot be ensured
Solution Approach 1:
The system merges the authentication process into a single simultaneous operation where both the drone and user device perform authentication checks at the same time. The drone captures the user's facial expressions and compares them against stored references, while the user device simultaneously captures the drone's visual appearance and compares it against stored references. This merged simultaneous authentication approach achieves mutual authentication without requiring separate sequential steps, maintaining balanced complexity while enhancing security.
4Speed
If real-time video processing is used for authentication, then authentication speed is improved, but computational resources and processing time are increased
Solution Approach 1:
The system extracts only the essential information needed for authentication from the full video stream. Instead of processing entire video clips, the system extracts key frames at specific moments when facial expressions occur, and further extracts only the facial embedding vectors from these frames. This extraction process reduces the amount of data that needs to be processed and compared, significantly reducing computational resource consumption while maintaining authentication speed and accuracy.
Solution Approach 2:
The system applies partial action by processing only the necessary portions of the video data. Rather than analyzing every frame continuously, the system processes video clips only when authentication is required, extracts embeddings only from relevant frames, and compares only the essential feature vectors. This partial processing approach reduces computational overhead while maintaining sufficient accuracy for secure authentication.
Data Source
AI summary
Described herein are systems and methods for secure and usable authentication techniques for drone delivery that leverages biometric information of faces, unlike traditional biometrics-based approaches, it does not need users to enroll their biometric information and is impervious to common drone attacks.