Multi-Frame Biometric Imaging for Ambient Light Removal
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Solution Overview
Problem
Existing image enhancement techniques struggle to effectively handle noise and illumination variations in biometric imaging, particularly in challenging environments, limiting the accuracy and reliability of biometric authentication processes.
Innovation Solution
A method involving multi-frame imaging captures both illuminated and unilluminated frames, applying noise reduction and super-resolution techniques to enhance image quality, utilizing polarimetric measurements and machine learning algorithms to improve biometric identification, including anti-spoof detection and facial recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multi-frame imaging with both illuminated and unilluminated frames is captured, then image quality and noise reduction are improved, but processing time and computational complexity increase
Solution Approach 1:
The system captures unilluminated frames in advance alongside illuminated frames, preparing reference data beforehand that can be quickly subtracted from illuminated frames to remove ambient light effects, thus reducing real-time processing requirements while maintaining high image quality
Solution Approach 2:
The processing is divided into distinct stages: capturing separate unilluminated and illuminated frame sets, subtracting unilluminated frames to remove ambient light, then applying noise reduction and super-resolution techniques to the processed frames, allowing each computational task to be optimized independently
2Measurement precision
If multiple image enhancing techniques are applied including noise reduction and super-resolution, then biometric identification accuracy is improved, but device complexity increases
Solution Approach 1:
Multiple image enhancement operations (noise reduction, super-resolution, illumination removal) are combined into a unified processing pipeline that operates on the multi-frame data, sharing computational resources and data structures across different enhancement tasks to reduce overall system complexity
Solution Approach 2:
A trained machine learning model serves as an intermediary that receives the multi-frame input data and automatically performs the sequence of enhancement operations, replacing complex manual processing algorithms with a single integrated neural network that handles multiple enhancement functions simultaneously
3Reliability
If externally-sourced illumination is removed from the on set of frames, then noise is reduced and image clarity is improved, but processing complexity increases
Solution Approach 1:
The external illumination component is extracted from the illuminated frames by subtracting the corresponding unilluminated frames, isolating only the light reflected from the subject's biometric features, thereby removing ambient light noise while maintaining a relatively simple pixel-level subtraction operation
4Reliability
If polarimetric measurements are performed on the captured frames, then anti-spoof detection capability is improved, but measurement time and processing load increase
Solution Approach 1:
Polarimetric measurements are performed periodically on specific frames within the captured sequence rather than continuously on all frames, selecting key frames that provide sufficient information for spoof detection while reducing overall measurement time and processing load
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
Enhances image resolution and reduces noise, improving the accuracy and reliability of biometric authentication processes by effectively handling environmental variations and spoof detection.
Implementation Method 1
The image sensor is configured to capture polarized images of the object in a set of near-infrared wavelengths
Implementation Method 2
an illuminator coupled to the image sensor
Data Source
AI summary
Systems and methods for performing multi-frame imaging are illustrated. One embodiment includes a method of performing biometric identification. The method captures an “off” and “on” set of frames. The “off” set of frames depicts an object when illuminated by externally-sourced illumination. The “on” set of frames depicts the object when illuminated by the externally-sourced illumination and an illuminator. The “off” and “on” set of frames are each polarized in a set of near-infrared wavelengths. The method performs an image enhancing technique to produce a denoised image. The image enhancing technique includes a multi-frame noise reduction technique based on a plurality of spatial aspects of image signals in both of: the “off” set of frames; and the “on” set of frames. The image enhancing technique removes the externally-sourced illumination from the “on” set of frames in producing the denoised image. The method performs an authentication based on the denoised image.