Non-Spatial Image Ranking and Grid Display for Biometric Identification
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Solution Overview
Problem
Conventional biometric identification systems require extensive manual review of multiple candidate images, leading to time-consuming and resource-intensive processes in security and forensic applications.
Innovation Solution
Implementing a system and method for efficient comparative non-spatial image data analysis, which ranks images based on attributes, displays them in a GUI window as an array of cells, and allows for simultaneous visual inspection, thereby accelerating the analysis process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual one-to-one examination of reference biometric image to each candidate biometric image is performed, then identification accuracy is maintained, but identification time and resource consumption increase significantly
Solution Approach 1:
The system segments the large set of candidate biometric images into multiple smaller groups or pages, each containing a manageable number of images (e.g., 10-20 images per page). This segmentation allows experts to review images in organized batches rather than overwhelming single-by-single examination, reducing overall review time while maintaining accuracy through systematic progression through segmented groups.
Solution Approach 2:
The system transitions from one-dimensional sequential review (one image at a time) to two-dimensional grid display (multiple images simultaneously arranged in rows and columns). This dimensional change enables experts to visually scan multiple candidate images at once, dramatically reducing identification time while preserving the ability to perform detailed one-to-one comparison when needed by selecting specific images for closer examination.
2Reliability
If multiple candidate biometric images are reviewed sequentially, then thorough examination is possible, but productivity and output per unit time decrease
Solution Approach 1:
The system performs preliminary automated processing of candidate biometric images before presenting them to experts. This includes pre-computing match scores, filtering obviously non-matching images, and organizing images by similarity metrics. This preliminary action reduces the total number of images requiring expert review and pre-arranges them in optimal sequences, allowing experts to focus their thorough examination efforts on the most promising candidates while maintaining high productivity.
Solution Approach 2:
The system displays multiple candidate images simultaneously in a grid layout, enabling experts to perform parallel visual scanning across multiple images. This two-dimensional presentation allows thorough comparative examination of facial features, biometric markers, and image qualities across several candidates at once, significantly increasing identification throughput without sacrificing examination depth, as experts can quickly eliminate obvious non-matches while retaining the ability to perform detailed analysis on selected images.
3Quantity of substance
If a large number of candidate biometric images are presented for review, then comprehensive candidate selection is achieved, but system complexity and operational difficulty increase
Solution Approach 1:
The system divides the large collection of candidate biometric images into multiple manageable pages or batches, each displaying a limited number of images (e.g., 10-20 per page). This segmentation reduces the cognitive load on experts and simplifies the user interface, making the system easier to operate despite handling thousands of candidate images overall. Experts can navigate through segmented pages systematically, and the system manages the complexity of large-scale image sets through organized pagination and progressive disclosure.
Solution Approach 2:
The system organizes numerous candidate images in a two-dimensional grid array rather than presenting them as a single overwhelming list. This spatial arrangement allows the interface to efficiently display many images (e.g., 16 or 25 per screen) in an organized, visually accessible format. The grid structure simplifies navigation and selection operations, reducing operational complexity despite the large quantity of candidate images being processed, as experts can easily scan, compare, and select images from the structured layout.
Data Source
AI summary
Systems (100) and methods (300, 400) for efficient comparative non-spatial image data analysis. The methods involve ranking a plurality of non-spatial images (1011, 1050, 1231, 1539, 0001, 0102, 0900, 1678, 0500, 0020, 0992, 1033, 1775, 1829) based on at least one first attribute thereof; generating a screen page (1102-1106) comprising an array (1206) defined by a plurality of cells (1208) in which at least a portion of the non-spatial images are simultaneously presented; and displaying the screen page in a first GUI window (802) of a display screen. Each cell comprises only one non-spatial image. The non-spatial images are presented in an order defined by the ranking thereof.