Biometric Verification via Infrared Subdermal Feature Analysis
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Solution Overview
Problem
Traditional identity verification methods, such as key-based and biometric systems, face challenges in ensuring accurate access control due to issues like key duplication, environmental limitations, and high false acceptance/false rejection rates, particularly in varying lighting conditions and population similarities.
Innovation Solution
A system utilizing an edge capture device that emits non-visible light, detects reflected and radiated light, and generates a biometric template by calculating weighted values and ranking distances to enhance facial recognition accuracy, reducing dependency on visible light and improving security through subdermal feature analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional biometric sensors are used in visible light conditions, then the system is simple and easy to operate, but the measurement precision deteriorates due to glares, shadows, and ambient lighting interference
Solution Approach 1:
The patent changes the wavelength parameter of light from visible spectrum to infrared spectrum. The edge capture device emits infrared light and detects infrared reflections, which penetrate skin to reveal subdermal facial features. This parameter change eliminates the problems of visible light (glares, shadows, ambient interference) while improving measurement precision of facial features.
2Reliability
If non-visible light detection is implemented to improve measurement precision, then false acceptance and false rejection rates are reduced, but the device complexity increases
Solution Approach 1:
The patent segments the facial recognition process into two distinct parts: visible light capture for general facial structure and infrared light capture for subdermal feature detection. The edge capture device includes both visible and infrared sensors, allowing the system to process multiple types of biometric data separately and combine them for more reliable authentication with reduced false acceptance and rejection rates.
Solution Approach 2:
The edge capture device is designed with multi-functionality, serving both as a visible light camera and an infrared sensor. This universal device can operate in different lighting conditions and capture multiple types of biometric information, improving reliability without requiring entirely separate systems for different functions.
3Reliability
If traditional keys or cards are used for access control, then the system is simple to implement, but security deteriorates due to key duplication and unauthorized access
Solution Approach 1:
The patent replaces mechanical access control systems (physical keys, cards, or biometric sensors) with an optical system based on infrared light detection. Instead of relying on physical objects that can be duplicated or lost, the system uses subdermal facial feature patterns captured through infrared imaging, which are inherently difficult to replicate and provide superior security.
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
The system provides robust and secure identity verification with reduced false positives/negatives, allowing keyless access and seamless credential management, enhancing security and accuracy across various environments and applications.
Implementation Method 1
A system utilizing an edge capture device that emits non-visible light, detects reflected and radiated light
Implementation Method 2
detects reflected and radiated light
Data Source
AI summary
Aspects of the present disclosure include methods for generating a sampled profile including a plurality of sampling points having a plurality of characteristic values associated with the detected non-visible light, identifying one or more macroblocks each includes a subset of the plurality of sampling points, calculating a number of occurrences of the local pattern value within each subset of the plurality of the sampling points for each of the one or more macroblocks, generating a first array including a plurality of weighted values by calculating the plurality of weighted values based on the numbers of occurrences of the local pattern value and corresponding sizes of the one or more macroblocks, assigning a unique index to each of the plurality of weighted values, generating a second array of the unique index by ranking the plurality of weighted values, and generating a third array including a plurality of ranking distances.