Biometric Group Identification Using Pre-Computed Templates
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Solution Overview
Problem
Conventional person identification systems are inefficient in identifying multiple individuals simultaneously and lack convenience in group identification processes, particularly in high-traffic areas such as entrances or monitoring systems.
Innovation Solution
A person identification device and method that includes a storage unit associating biometric information with group information, an information obtaining unit, a detection unit, an identification unit, a group judgment unit, and an output unit, enabling the efficient identification of groups by collating biometric data and determining group actions based on predetermined conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional person identification systems process multiple individuals one by one, then identification accuracy is maintained, but processing efficiency and speed deteriorate in high-traffic environments
Solution Approach 1:
The system segments the identification process into two distinct phases: a batch learning phase where the system learns group structures from registered biometric data, and a rapid recognition phase where pre-learned group templates are used for quick identification. This segmentation allows complex group analysis to be performed beforehand, enabling fast real-time processing without compromising accuracy.
Solution Approach 2:
The system performs preliminary action by pre-processing and storing group-level biometric templates during the registration phase. These pre-computed group templates capture the statistical characteristics of multiple individuals, allowing the system to rapidly classify new individuals into groups without performing exhaustive comparisons in real-time, thus significantly reducing identification time.
2Measurement precision
If detailed group analysis is performed for every individual, then group identification accuracy is improved, but system complexity and processing load increase
Solution Approach 1:
The system performs detailed group analysis in advance during the registration phase, computing group-level biometric templates that capture statistical characteristics of multiple individuals. These pre-computed templates store essential group information without requiring complex real-time analysis, thus maintaining high identification accuracy while reducing system complexity during operation.
Solution Approach 2:
The system creates simplified copies of group characteristics in the form of biometric templates during registration. These templates are mathematical representations that capture the essential features of group members, allowing accurate group identification without needing to store or process all original detailed biometric data during recognition, thereby reducing system complexity.
3Measurement precision
If individual biometric data is stored for each person, then identification precision is maintained, but data storage requirements and processing overhead increase
Solution Approach 1:
The system merges individual biometric data into group-level templates by computing statistical characteristics (mean, covariance) across multiple individuals. This combining process creates compact representations that capture essential group variations with far fewer parameters than storing complete individual biometric datasets, thus maintaining identification precision while significantly reducing storage requirements.
Solution Approach 2:
The system transforms detailed individual biometric parameters into condensed statistical parameters (mean vectors and covariance matrices) that represent group characteristics. This parameter transformation reduces the dimensionality and volume of stored data while preserving the essential information needed for accurate identification, achieving a balance between precision and storage efficiency.
Data Source
AI summary
A person identification device obtains information including biometric information of a person, detects the biometric information of at least one person from the obtained information, collates each detected biometric information with the biometric information of at least one registrant associated with group information and stored in a storage unit to thereby identify the person having the biometric information detected from the obtained information, classifies a plurality of successively identified persons into group candidates based on predetermined conditions, divides the persons of the group candidates into groups based on the group information of each person stored in the storage unit, and outputs a grouping result to an external device.