Face Feature Vector Encoding for 3D Identity Verification
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Solution Overview
Problem
Existing biometric security methods, such as fingerprint and two-dimensional facial recognition, are unreliable and insecure on small electronic devices, making it difficult to securely authenticate users accessing accounts, and users often prefer more convenient methods.
Innovation Solution
A facial recognition authentication system using a mobile device camera to capture enrollment and authentication images while moving relative to the user's head, incorporating multiple sensors and image processing to detect facial features and movement, with additional security layers like banding, glare, and stereoscopic imaging, and optional QR code detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If two-dimensional facial recognition is used for secure access, then the system is easier to implement, but the security reliability deteriorates because faces can be photographed or recorded and used to trick the system
Solution Approach 1:
The patent transitions from two-dimensional facial recognition to three-dimensional facial recognition by capturing multiple images from different angles and distances. This adds the dimension of depth and spatial information, making it impossible for attackers to use photographs or videos for authentication, as these flat representations cannot replicate the three-dimensional facial geometry captured from multiple viewpoints.
Solution Approach 2:
The system performs preliminary actions by capturing a comprehensive set of facial images from multiple angles and distances during the enrollment phase, creating a three-dimensional facial model. This preliminary comprehensive capture ensures that during authentication, only the complete three-dimensional facial geometry can be verified, preventing fraud before it can succeed.
2Reliability
If long passwords meeting various criteria are required for secure access, then the security reliability is improved, but the ease of operation deteriorates making it cumbersome to type into small electronic devices
Solution Approach 1:
The patent replaces the mechanical action of typing long passwords with optical recognition through three-dimensional facial scanning. Instead of requiring users to manually input complex password combinations on small screens, the system uses the camera to capture and analyze the user's three-dimensional facial geometry, eliminating the need for manual typing while maintaining high security.
Solution Approach 2:
The system performs self-service authentication by automatically capturing and analyzing the user's three-dimensional facial geometry without requiring manual input. The facial recognition system autonomously verifies identity through biometric analysis, eliminating the need for users to manually type passwords or provide other form of manual authentication.
3Ease of operation
If fingerprint systems are used on small electronic devices, then the ease of operation is improved, but the reliability deteriorates making them unreliable and unsecure
Solution Approach 1:
The patent applies three-dimensional facial recognition instead of two-dimensional fingerprint scanning. By capturing facial geometry from multiple angles and distances, the system creates a comprehensive three-dimensional model that is much more difficult to replicate than a flat fingerprint image, thereby improving security reliability while maintaining ease of operation through automatic recognition.
Data Source
AI summary
A method and system for establishing an association between a document and a person or verifying identity comprising capturing images of the person's face and processing the images to generate a face feature vector data. The face feature vector data cannot be reverse processed to generate the image of the person's face. The method also captures images of the graphic on the item and processes those images to extract graphic derived feature vector data that is stored inside the graphic code. The graphic extracted face feature vector data cannot be reverse processed to generate the image of the person's face. Then, the face derived feature vector data is compared to the graphic derived feature vector data to develop a similarity value. Then the similarity value is compared to a first threshold value to develop a validation indicator representing the likelihood that the graphic/item are associated with the person.