Lensless Camera Feature Recognition via Masked Frequency Domain Processing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The installation of cameras poses a significant privacy risk to users and potential legal liability for content providers, leading to reluctance in using cameras due to concerns about image reconstruction and privacy invasion.
Innovation Solution
A lensless camera system with a masked lens that distorts images using a private pattern, combined with a machine learning model trained on frequency domain image data, enables feature detection (such as face recognition) without reconstructing the undistorted image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a camera is installed to enable feature detection and authentication, then the functionality and user experience are improved, but privacy risks and legal liability increase
Solution Approach 1:
The patent segments the image processing pipeline into two distinct parts: (1) a masked lensless camera that captures only distorted frequency domain data without reconstructing the original image, and (2) a machine learning model that processes this distorted data for feature detection. This segmentation allows the system to extract useful information (face presence, gestures) while preventing reconstruction of the original image, thereby reducing privacy risks while maintaining functionality.
Solution Approach 2:
The patent introduces a mask with a private pattern as an intermediary between the camera sensor and the image processing system. This mask distorts the captured image data in a controlled manner, transforming it into frequency domain data that retains sufficient information for feature detection but loses the ability to reconstruct the original image. The mask acts as a mediator that enables functionality while protecting privacy.
2Object-affected harmful factors
If a masked lensless camera is used to distort images for privacy protection, then privacy protection is improved, but image reconstruction capability deteriorates
Solution Approach 1:
The system applies partial action by deliberately destroying only the portion of image information needed for reconstruction (spatial domain data), while preserving the portion needed for feature detection (frequency domain characteristics). The mask distortion is designed to be excessive for reconstruction purposes but insufficient for eliminating all useful information, achieving privacy protection without complete information loss.
3Measurement precision
If a machine learning model processes distorted frequency domain data for feature detection, then accuracy is improved, but the ability to reconstruct undistorted images is reduced
Solution Approach 1:
The patent changes the parameter domain from spatial domain (original image coordinates) to frequency domain (transformed data). By applying a transformation function (such as Fourier transform) to the masked image data, the system converts it into frequency domain data that preserves essential features for detection while making reconstruction impossible. The machine learning model is trained specifically on this transformed data to maintain detection accuracy.
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 solution allows for accurate image feature detection while maintaining strong privacy protections, as the system cannot reconstruct the original image without access to the private pattern data.
Implementation Method 1
a lensless camera (e.g., a complementary metal-oxide-semiconductor (CMOS) sensor) for capturing an image of an environment (e.g., by converting detected photons into electric signals)
Implementation Method 2
The pattern of the mask may distort (e.g., blur) the captured image data, for example by casting a shadow on the light-sensing part of the camera
Implementation Method 3
The pattern of the mask may distort (e.g., blur) the captured image data, for example by casting a shadow on the light-sensing part of the camera and/or by causing refraction in the incoming light
Data Source
AI summary
Systems and methods are described for generating pixel image data, using a lensless camera, based on light that travels through a mask that with pattern masking the lensless camera. The system applies a transformation function to the pixel image data to generate frequency domain image data. The system inputs the frequency domain image data into a machine learning model, wherein the machine learning model does not have access to data that represents the pattern of the mask. The model is trained using a set of images with the feature that are captured by the flat, lensless camera through the mask. The system processes the frequency domain image data using the machine learning model to determine whether the pixel image data depicts the image feature. The system further performs an action based on determining that the pixel image data depicts the image feature.