Biometric Gallery Segmentation for Faster Facial Identification
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Solution Overview
Problem
Biometric identification systems face challenges with accuracy, latency, and throughput when dealing with large galleries, particularly with 'low-touch' biometrics like facial recognition, which are impacted by external factors and degrade in performance as gallery size increases.
Innovation Solution
Implementing intelligent gallery management (IGM) techniques to derive application-specific biometric galleries by pulling information for relevant subsets of populations, reducing the size of the gallery used for identification, and using these subsets for biometric identification, thereby maintaining accuracy and reducing resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a large biometric gallery is used for identification, then the system can identify more people, but the identification time increases and accuracy decreases
Solution Approach 1:
The patent segments the large biometric gallery into multiple smaller sub-galleries based on application-specific criteria. Each sub-gallery contains only the biometric data relevant to a particular application, allowing faster and more accurate identification by searching smaller datasets instead of the entire large gallery.
Solution Approach 2:
The patent extracts and removes irrelevant biometric data from the large gallery to create streamlined application-specific galleries. By taking out only the necessary subset of biometric information needed for each application, the system reduces search time and improves identification accuracy without sacrificing the ability to identify relevant individuals.
2Quantity of substance
If a large biometric gallery is used for identification, then the system can identify more people, but the computational resources required increase
Solution Approach 1:
The patent segments the large biometric gallery into multiple smaller sub-galleries based on application-specific criteria. Each sub-gallery contains only the biometric data relevant to a particular application, allowing faster and more accurate identification by searching smaller datasets instead of the entire large gallery.
Solution Approach 2:
The patent extracts and removes irrelevant biometric data from the large gallery to create streamlined application-specific galleries. By taking out only the necessary subset of biometric information needed for each application, the system reduces search time and improves identification accuracy without sacrificing the ability to identify relevant individuals.
3Adaptability or versatility
If the entire biometric gallery is used for all applications, then all people can be identified, but the system complexity increases
Solution Approach 1:
The patent segments the large biometric gallery into multiple smaller sub-galleries based on application-specific criteria. Each sub-gallery contains only the biometric data relevant to a particular application, allowing faster and more accurate identification by searching smaller datasets instead of the entire large gallery.
Solution Approach 2:
The patent implements a dynamic gallery management system that automatically selects and configures the appropriate sub-gallery based on the specific application being executed. This dynamic adaptation allows the system to maintain versatility across multiple applications while keeping the operational complexity low by only loading and processing relevant subsets of biometric data for each application.
Data Source
AI summary
A system provides intelligent gallery management for biometrics. A first gallery is obtained that includes biometric and/or other information on a population of people. An application is identified. A subset of the population of people is identified based on the application. A second gallery is derived from the first gallery by pulling the information for the subset of the population of people without pulling the information for the population of people not in the subset. Biometric identification (such as facial recognition) for the application may then be performed using the second gallery rather than the first gallery. In this way, the system is improved as less time is required for biometric identification, fewer device resources are used, and so on.