Face Recognition for Unkeyed Image Identification
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Solution Overview
Problem
Conventional search systems are unable to identify and return images of individuals from large collections, such as the web, when these images are not keyed with metadata or tags, as they lack information associating the person's name.
Innovation Solution
A method involving face detection using a face detection algorithm to identify representative face images, generating face models, and performing face matching to associate additional images with named entities, allowing for the identification and labeling of images without prior metadata, enabling comprehensive search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional search systems rely on metadata or tags to identify images, then search accuracy for keyed images is improved, but the system cannot identify un-keyed images without metadata
Solution Approach 1:
The patent introduces face detection algorithms and face models as intermediary tools between the search query and the image database. Instead of directly searching for images with metadata, the system uses face detection to identify faces in images, extracts face features, and matches them against stored face models to retrieve relevant images, thereby bridging the gap between keyed and un-keyed images
Solution Approach 2:
The patent replaces the manual metadata tagging mechanism with automated face detection and recognition technology. Instead of relying on human-generated tags or metadata, the system automatically detects faces, extracts features, and performs matching, substituting the mechanical tagging process with an automated biometric recognition system
2Measurement precision
If face detection algorithms are applied to all retrieved images, then comprehensive face identification is achieved, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary face detection on a subset of retrieved images to identify faces and extract features before conducting full-scale face matching. By pre-processing and pre-identifying faces in representative images, the system creates face models that can then be used for faster matching against the broader image collection, reducing the need to perform exhaustive face detection on every single image
Solution Approach 2:
The patent applies face detection to a selective portion of images rather than uniformly to all images. The system identifies representative images from the retrieved set, performs face detection on these, and uses the resulting face models for matching, thereby achieving comprehensive identification coverage while limiting the computational burden to a manageable subset
3Measurement precision
If manual tagging of images with person names is performed, then image association accuracy is improved, but labor costs and time requirements increase
Solution Approach 1:
The patent enables the system to automatically perform image tagging through face detection and recognition without human intervention. The system detects faces, extracts features, matches them against existing face models or creates new ones, and automatically associates image filenames with person names, making the tagging process self-service and eliminating the need for manual human labor
Solution Approach 2:
The patent replaces the manual human tagging process with automated face recognition technology. Instead of human operators reviewing and tagging images, the system uses algorithms to detect faces, extract features, perform matching, and generate associations automatically, substituting human mechanical tagging with automated computational processing
Data Source
AI summary
A method includes identifying a named entity, retrieving images associated with the named entity, and using a face detection algorithm to perform face detection on the retrieved images to detect faces in the retrieved images. At least one representative face image from the retrieved images is identified, and the representative face image is used to identify one or more additional images representing the at least one named entity.