Liveness Verification Using Multi-Modal Image Analysis
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Solution Overview
Problem
Current user authentication systems face challenges in effectively distinguishing between genuine and fake faces, particularly with the use of photos, moving pictures, and masks, as existing anti-spoofing techniques may not adequately verify the liveness of the face input.
Innovation Solution
A processor-implemented liveness verification method that involves obtaining multiple images, such as color and infrared images, and selecting appropriate liveness models based on detected object parts to verify the liveness of the object by calculating liveness scores using various models, including color verification models, IR region models, and part comparison models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing anti-spoofing techniques use single modality images and basic features (LBPs, HOG, DoG), then the system complexity is low, but the accuracy in distinguishing genuine and fake faces deteriorates
Solution Approach 1:
The patent segments the verification process into multiple independent liveness models, each specialized for detecting specific spoofing artifacts. Different models analyze different aspects (e.g., texture patterns, depth information, thermal characteristics) and their results are combined to make the final verification decision. This segmentation allows the system to achieve high accuracy without requiring a single overly complex model.
Solution Approach 2:
The patent merges multiple image modalities (visible light, infrared, depth maps) and multiple feature extraction methods into a unified verification framework. By combining complementary information from different modalities and models, the system achieves superior liveness detection accuracy that exceeds what any single modality or method could provide alone.
2Reliability
If multiple liveness models and multiple image modalities are used to verify liveness, then the accuracy of differentiating real and fake faces is improved, but the device complexity increases
Solution Approach 1:
The patent implements a dynamic model selection mechanism that adapts the verification process based on the input characteristics. The system dynamically selects which liveness models to apply and which image modalities to use based on the detected object parts and initial analysis results. This dynamic adaptation allows the system to maintain high reliability while reducing unnecessary computational overhead for each verification case.
Solution Approach 2:
The patent introduces an intermediary layer that coordinates between multiple liveness models and image modalities. This intermediary manages the complex interactions by orchestrating model execution, aggregating results, and making the final verification decision. It acts as a mediator that simplifies the overall system architecture while enabling the use of multiple sophisticated models.
3Measurement precision
If liveness verification uses multiple image patches from different modalities and models, then the measurement precision of liveness score is improved, but the loss of time for processing increases
Solution Approach 1:
The patent performs preliminary analysis of the input images to identify key object parts and potential spoofing indicators before applying the full set of liveness models. Based on this preliminary assessment, the system pre-selects which models and image modalities are most relevant for the specific verification case, avoiding unnecessary processing of all possible models and reducing overall processing time while maintaining precision.
Solution Approach 2:
The patent implements a tiered verification approach where a subset of liveness models is applied first to perform partial verification. If the initial checks provide sufficient confidence, the verification process stops early. Only when needed does the system apply the complete set of models, thus achieving high precision when necessary while minimizing processing time for clear cases.
Data Source
AI summary
Provided is a liveness verification method and device. A liveness verification device acquires a first image and a second image, and select one or more liveness models based on respective analyses of the first image and the second image, including analyses based on an object part being detected in the first image and/or the second image, and to verify, using the selected one or more liveness models, a liveness of the object based on the first image and/or the second image. The first image may be a color image and the second image may be an Infrared image.