Biometric Matching Module Memory Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Facial recognition systems with large numbers of image capture devices and databases face significant delays in processing and management, making real-time matching challenging, particularly in security applications where swift identification is crucial.
Innovation Solution
Implementing a biometric matching module (BMM) that utilizes machine learning techniques, such as neural networks, and allocates a large contiguous memory space to optimize processing, combined with parallel processing methodologies like single instruction multiple data (SIMD) and batching of match requests, to facilitate rapid comparison of biometric data across large datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a large number of image capture devices and a large database are used, then the coverage and capability of facial recognition are improved, but the processing time increases significantly
Solution Approach 1:
The patent segments the facial recognition system into multiple independent components: a biometric matching module for core matching operations, a neural network module for embedding generation, and separate processing units for different functions. This segmentation allows each component to process data independently and efficiently, reducing overall processing time while maintaining comprehensive coverage capability.
Solution Approach 2:
The patent transitions from traditional 2D image processing to 3D spatial processing by generating facial embeddings and storing them in a three-dimensional embedding space. This dimensional transformation enables efficient similarity search and matching operations, allowing the system to handle large databases quickly through vector space relationships rather than pixel-by-pixel comparison.
2Speed
If a large contiguous memory space is allocated for machine learning processing, then the processing speed is improved, but the memory resource consumption increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing images to generate facial embeddings and storing them in a structured embedding database before actual recognition tasks. This pre-computation and pre-organization of data in a optimized memory structure enables rapid retrieval and matching operations during real-time recognition, achieving fast processing without requiring excessive memory capacity during active processing.
Solution Approach 2:
The patent changes the parameter representation from raw image data to compressed facial embeddings. By transforming images into lower-dimensional embedding vectors and organizing them in a three-dimensional space, the system reduces memory requirements while maintaining recognition accuracy. This parameter transformation enables efficient storage and fast similarity search operations.
3Productivity
If parallel processing methodologies like SIMD and batching are implemented, then the computational efficiency is improved, but the system complexity increases
Solution Approach 1:
The patent merges multiple processing operations into unified modules: the biometric matching module combines facial detection, embedding extraction, and similarity matching operations into a single integrated processing unit. This consolidation enables parallel execution of multiple tasks simultaneously while maintaining a relatively simple and manageable system architecture, improving computational efficiency without proportionally increasing complexity.
Solution Approach 2:
The neural network module serves multiple functions: it extracts facial embeddings from images, generates recognition features, and supports both training and inference operations. This multi-functionality reduces the need for separate specialized components, simplifying the overall system architecture while maintaining high computational efficiency through unified processing.
Data Source
AI summary
Technologies and implementations for facilitating machine learning of processing and management of biometric matching. The technologies and implementations may include a biometric matching module (BMM). The BMM may facilitate converting biometric digital data into embeddings, and prior to determining a match (i.e., biometric recognition), the BMM may move embeddings to memory or memory pools close to one or more processors.