Facial Recognition Feature Vector Extraction via Distributed Web Crawling

The patent divides the centralized database system into distributed databases across multiple servers. Each server maintains a portion of the facial image database, allowing the system to achieve comprehensive coverage without requiring a single costly centralized infrastructure. This segmentation reduces the complexity and cost barrier while maintaining identification accuracy through distributed query capabilities.

The patent creates a universal facial recognition platform that serves multiple functions: identification, verification, and information retrieval. The system can handle various types of facial images (real-time captures, photographs, videos) and perform different operations (matching against databases, extracting features, comparing images) through a unified architecture, making the system more cost-effective and accessible while maintaining high accuracy.

The patent implements preliminary database population through web crawling and automated image collection from public sources. Before formal identification operations begin, the system proactively builds comprehensive facial image databases by scraping images from social media, public records, and other online sources. This preliminary action ensures reliable identification capability is established in advance, while the system remains versatile enough to handle new individuals by continuously updating the database.

The patent creates a dynamic database system that continuously updates and adapts. Rather than relying on static pre-existing databases, the system dynamically adds new facial images as they become available from various sources, and adapts its matching algorithms based on accumulated data. This dynamic approach maintains high reliability through continuous learning while preserving versatility to identify any individual who appears in the evolving database.

The patent performs preliminary feature extraction and database indexing before actual identification queries. Facial images in the database are pre-processed to extract key features (landmarks, eigenfaces, histograms) and store them in optimized formats. When a real-time identification query arrives, the system compares pre-computed features rather than processing full images, dramatically reducing computational complexity while maintaining real-time speed.

The patent extracts and separates the computationally intensive feature extraction step from the real-time matching operation. By taking out the heavy processing of feature detection, landmark identification, and image normalization as preliminary steps, the system reduces the real-time computational burden to simple feature comparison operations. This extraction allows real-time identification speed while managing computational complexity through staged processing.