Biometric Image Spoof Detection with 3D-Semantic Fusion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing anti-spoofing detection systems in face recognition rely on a single type of information, such as three-dimensional data, making them prone to spoof attacks and lacking accuracy and reliability, and often require complex and expensive hardware or user collaboration.
Innovation Solution
A method that combines three-dimensional and semantic information from images to differentiate between real objects and their spoofs, using a computing device with a processor, image sensor, and storage, employing classifiers like neural networks to enhance reliability and reduce hardware complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple dedicated camera systems are used for anti-spoofing detection, then measurement precision and reliability improve, but device complexity and cost increase
Solution Approach 1:
The patent merges multiple types of information (three-dimensional data, texture information, color information, and reflection information) into a unified analysis framework. This combination allows the system to achieve high spoof detection accuracy by processing diverse data types through a single integrated system rather than requiring separate dedicated camera systems for each type of information.
Solution Approach 2:
The system employs a single camera that captures multiple types of information simultaneously (three-dimensional structure, texture, color, and reflection properties). This multi-functional approach allows one device to perform what previously required multiple specialized cameras, reducing hardware complexity while maintaining detection precision.
2Device complexity
If a single camera is used to generate a 3D model, then device complexity reduces, but measurement precision and reliability deteriorate
Solution Approach 1:
The patent combines multiple information types (three-dimensional structure, texture patterns, color characteristics, and reflection properties) extracted from single-camera images. By merging these diverse data sources, the system compensates for the limitations of using a single camera and achieves measurement precision comparable to multi-camera systems.
Solution Approach 2:
The system transitions from relying solely on three-dimensional geometric information to incorporating additional dimensions of analysis including texture patterns, color characteristics, and reflection properties. This multi-dimensional approach enriches the data available for spoof detection, enabling accurate differentiation between real and spoof objects even with a single camera.
3Device complexity
If only three-dimensional information is used for spoof detection, then processing simplicity improves, but reliability deteriorates due to susceptibility to spoof attacks
Solution Approach 1:
The patent merges multiple types of information (three-dimensional data, texture information, color information, and reflection information) into a unified analysis framework. This combination allows the system to achieve high spoof detection accuracy by processing diverse data types through a single integrated system rather than requiring separate dedicated camera systems for each type of information.
Solution Approach 2:
The system creates a composite information structure that integrates four distinct types of data (geometric three-dimensional information, texture patterns, color characteristics, and reflection properties). This composite approach is analogous to using composite materials in engineering, where combining different material properties creates a system that is more robust and reliable than any single component alone.
4Measurement precision
If advanced 3D camera systems are deployed, then measurement precision improves, but ease of operation and accessibility worsen
Solution Approach 1:
The patent achieves the functional capabilities of expensive 3D camera systems by processing standard two-dimensional images through advanced computational algorithms. Instead of requiring users to possess or interact with specialized hardware, the system creates a computational copy of 3D analysis capabilities that can be deployed on conventional devices, greatly improving accessibility and ease of operation.
Solution Approach 2:
The system replaces complex mechanical 3D camera hardware with computational image processing algorithms. By substituting physical hardware complexity with software-based analysis, the patent enables spoof detection capabilities on standard smartphones and computing devices, eliminating the need for specialized cameras and making the technology universally accessible.
Data Source
AI summary
A method for differentiating a real object in an image from a spoof of the real object, the method comprising obtaining an image comprising at least one object, wherein the object comprises at least one biometric identifier, such as a finger, a fingerprint, a face or a palm, extracting three-dimensional information and semantic information from the image, wherein the semantic information relates the at least one object in the image to the at least one biometric identifier and/or relates different objects in the image to each other, merging the extracted three-dimensional and semantic information to a combined information, processing the combined information by a classifier, and outputting by the classifier a data set which indicates whether the at least one object in the image is the real object or a spoof of the real object.