Biometric Identification via 3D Imaging and AI
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Solution Overview
Problem
Current data collection and identification systems face challenges in accurately and securely identifying subjects across various environments, including airports, medical facilities, and secure entry points, due to limitations in handling noisy data and accounting for physiological and morphological changes over time.
Innovation Solution
The system employs an imaging apparatus connected to a network with a database that captures and compares image data, using machine learning and AI techniques like deep neural networks to identify subjects based on three-dimensional imaging and biometric data, while accounting for expected changes and noise levels, and utilizing blockchain for secure data storage and privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data collection and identification systems are used, then device complexity is reduced, but measurement precision and reliability deteriorate due to inability to handle noisy data and account for physiological changes over time
Solution Approach 1:
The system segments the identification process into multiple independent modules: data collection from multiple sources (imaging apparatus, biometric sensors), noise filtering module, physiological change compensation module, and identification algorithm module. Each module handles a specific aspect of the problem, allowing complex processing to be broken down into manageable components that can be optimized independently.
Solution Approach 2:
The system performs preliminary actions by collecting and storing baseline biometric data and physiological parameters before identification is needed. Machine learning models are pre-trained on datasets that include various physiological states and noise conditions. This preliminary preparation enables the system to quickly adapt to new conditions without requiring complex real-time processing of all parameters.
2Reliability
If machine learning and AI techniques are employed to account for physiological and morphological changes, then identification reliability improves, but computing energy consumption and processing time increase
Solution Approach 1:
The system applies partial action by using machine learning models selectively - only for the specific physiological and morphological changes that most impact identification accuracy. Not all possible physiological parameters are processed through complex ML models; instead, the system identifies and focuses on the most critical changes (such as aging-related morphological changes, temporary physiological states) while using simpler methods for less impactful parameters, thereby reducing overall computational energy consumption.
Solution Approach 2:
The system changes parameters by transforming complex physiological data into simplified feature representations that capture essential identification information while reducing computational burden. Machine learning models are trained to extract key discriminative features rather than processing raw physiological data in full detail, allowing reliable identification with reduced energy consumption.
3Measurement precision
If multiple data sources and machine learning models are integrated, then measurement precision improves, but device complexity and data processing requirements worsen
Solution Approach 1:
The system merges multiple data sources (imaging data, biometric measurements, physiological parameters) into a unified identification framework. Rather than maintaining separate processing pipelines for each data type, the patent integrates them into a cohesive model where all data sources contribute to a single identification decision, reducing overall system complexity while maintaining high measurement precision.
Solution Approach 2:
The patent introduces intermediary components such as feature extraction layers and data normalization modules that act as mediators between raw data from multiple sources and the final identification algorithm. These intermediaries standardize different data formats and extract relevant features, simplifying the integration process and reducing the complexity of handling heterogeneous data sources.
Data Source
AI summary
Biological organs and tissues can be identified using imaging or other data representing the organs and tissues. Example imaging modalities include 3D x-rays (including CT scans), MRI imaging, and millimeter wavelength scanning commonly used for airport security. Biomarkers may be identified as part of daily activities, such as airport travel, applying for government identifications (licenses and passports), medical appointments, and fitness monitoring. These imaging approaches may create static and dynamic data sets for comparison against existing data sets in a database. Biomarkers may identify (and predict) normal, morphological or morbidity changes over time. Such imaging biomarkers may securely identify individuals at critical checkpoints such as airports and border crossings. This approach is also applicable to plant identification and can provide a secure chain of custody for virtually any object.