Liveness Detection Using Depth Map Quality Assessment
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Solution Overview
Problem
Face recognition technologies are vulnerable to spoofing attacks using physical or digital images, requiring effective liveness detection to differentiate between living and non-living entities.
Innovation Solution
A method and apparatus for liveness detection using a depth sensor to acquire depth maps and an image sensor to capture target images, with quality detection and neural network processing to determine the liveness of a target object based on the quality of the depth map and image information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If face recognition technology is implemented, then convenience and functionality are improved, but vulnerability to spoofing attacks increases
Solution Approach 1:
The patent introduces depth information as an intermediary element between the target object and the recognition system. By capturing depth maps using a depth sensor and comparing depth features with the target image, the system creates an additional verification layer that prevents spoofing attacks while maintaining convenient face recognition functionality
Solution Approach 2:
The patent transitions from two-dimensional image-based recognition to three-dimensional depth-aware recognition. By incorporating depth maps and analyzing depth features such as depth edges and depth contours, the system adds a spatial dimension that effectively distinguishes real faces from spoofing attempts
2Measurement precision
If depth map quality detection is performed, then liveness detection accuracy is improved, but system complexity increases
Solution Approach 1:
The patent performs quality detection on depth maps before using them for liveness detection. By pre-assessing depth map quality metrics such as depth edge clarity and depth contour completeness, the system ensures high-quality input data for accurate liveness detection while maintaining a structured and manageable processing workflow
Solution Approach 2:
The patent divides the liveness detection process into distinct modules: depth map acquisition, quality detection, feature extraction, and final liveness determination. This segmentation allows each module to be optimized independently, improving overall accuracy while keeping system complexity manageable through modular design
Data Source
AI summary
A method for liveness detection includes: acquiring a first depth map captured by a depth sensor and a first target image captured by an image sensor; performing quality detection on the first depth map to obtain a quality detection result of the first depth map; and determining a liveness detection result of a target object in the first target image based on the quality detection result of the first depth map. The present disclosure can improve the accuracy of liveness detection.