Multi-lens Camera Anti-spoofing via Depth and Infrared Fusion
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Solution Overview
Problem
Existing face detection technologies require users to perform random actions, leading to prolonged detection times and poor user experiences, as they struggle to differentiate between living beings and counterfeit representations effectively.
Innovation Solution
An anti-counterfeiting face detection method utilizing a multi-lens camera with a TOF camera and an RGB camera, which acquires depth, infrared, and RGB images, analyzes these images using pre-trained deep learning models to determine if the face meets preset rules, thereby identifying living bodies without user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users perform random actions (blinking, shaking head, reading numbers) for detection, then the system can verify living body status, but the detection time becomes long and user experience deteriorates
Solution Approach 1:
The system performs preliminary acquisition of depth image, infrared image, and RGB image before the actual detection judgment. By pre-acquiring multi-dimensional data (depth information from TOF camera, infrared characteristics, and visible light information), the system prepares all necessary verification materials in advance, enabling rapid living body authentication without requiring users to perform time-consuming actions during the detection phase
Solution Approach 2:
The patent introduces an intermediary multi-lens camera system comprising TOF camera, infrared camera, and RGB camera that mediates between the user and the detection system. This intermediary device automatically captures depth information, infrared characteristics, and visual information simultaneously, replacing the need for direct user interaction (such as blinking or reading numbers) while maintaining high reliability in living body verification
2Measurement precision
If multiple image types (depth, infrared, RGB) are acquired and analyzed simultaneously, then the accuracy of distinguishing living bodies from counterfeit representations is improved, but the device complexity increases
Solution Approach 1:
The patent merges multiple camera functions into an integrated multi-lens camera system that simultaneously captures depth information (TOF), infrared characteristics, and RGB images. By combining these different sensing modalities into a single unified device with coordinated acquisition and processing, the system achieves high measurement precision for distinguishing living bodies from counterfeit representations while managing device complexity through integrated design
Solution Approach 2:
The multi-lens camera system exhibits multi-functionality by performing multiple detection tasks simultaneously: depth measurement, infrared thermal characteristic analysis, and visible light face recognition. This universal device can handle various verification scenarios (living body detection, face authentication, depth verification) with a single integrated system, improving measurement precision across multiple dimensions without proportionally increasing complexity
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly reduces detection time, enhances user experience, and improves the accuracy of distinguishing between living bodies and counterfeit representations by simultaneously analyzing depth, infrared, and visible light information.
Implementation Method 1
acquiring a depth image, an infrared image and an RGB image by using the TOF camera and the RGB camera
Data Source
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AI summary
Embodiments of the present application provide an anti-counterfeiting face detection method, device and multi-lens camera, wherein the anti-counterfeiting face detection method comprises: acquiring a depth image, an infrared image and an RGB image by using a TOF camera and an RGB camera; analyzing the RGB image through a preset face detection algorithm to determine an RGB face region of a face in the RGB image and position information of the RGB face region; determining a depth face region of the face in the depth image and an infrared face region of the face in the infrared image based on the position information of the RGB face region; determining that the face passes the detection when the depth face region, the infrared face region and the RGB face region meet corresponding preset rules respectively. In the anti-counterfeiting face detection method in the embodiment of the present application, the detection of a living body face can be completed without the cooperation of the user performing corresponding actions, which can save the detection time and provide good user experience.