Facial Recognition Feature Vector Matching via Distributed Personal Databases
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current facial recognition technologies lack a user-friendly and cost-effective method for determining which celebrity a person resembles, especially using digital images captured by wireless communication devices.
Innovation Solution
A system that processes digital images into feature vectors, compares them against a database of celebrity feature vectors, and returns the closest match along with associated metadata, using algorithms like principle component analysis and incorporating human perception through voting to enhance matching accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If facial recognition software uses centralized databases with complex algorithms to identify individuals, then identification accuracy is improved, but system complexity and cost increase
Solution Approach 1:
The system divides the centralized database into distributed personal image databases, where each user stores their own reference images locally. This segmentation reduces the complexity of maintaining a single large centralized database while preserving identification accuracy through local pattern recognition.
Solution Approach 2:
The patent implements self-service by allowing users to automatically enroll their own facial images without manual intervention. The system captures multiple images of the user, processes them through pattern recognition algorithms, and stores the resulting biometric templates in the user's personal database, eliminating the need for operators to manually create and manage database entries.
2Measurement precision
If the system processes and compares facial images against large databases, then identification accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary processing by capturing multiple facial images during enrollment and pre-computing biometric templates before actual identification is needed. These pre-processed templates are stored in personal databases, enabling rapid comparison during identification without requiring real-time processing of raw images, thus reducing processing time while maintaining accuracy.
Solution Approach 2:
The patent uses partial action by comparing only the essential biometric features extracted from pre-processed templates rather than analyzing complete high-resolution images during identification. This selective comparison of key facial characteristics maintains matching accuracy while significantly reducing the computational time required for database searches.
3Reliability
If the system stores detailed facial image data in centralized databases, then identification reliability improves, but security risks and data privacy concerns increase
Solution Approach 1:
The patent segments the centralized storage model into distributed personal databases, where each user's biometric data is stored locally rather than in a single centralized repository. This segmentation reduces security risks by eliminating a single point of failure and limiting the impact of potential breaches to individual user data only, while maintaining identification reliability through secure local storage.
Solution Approach 2:
The system introduces an intermediary layer of encrypted biometric templates between the original facial images and the identification process. Instead of storing and processing raw facial images, the patent converts images into encrypted template representations that preserve identification capability while protecting underlying personal data, thus reducing security risks associated with storing detailed facial information.
Data Source
AI summary
A method and system for matching an unknown facial image of an individual with an image of a celebrity using facial recognition techniques and human perception is disclosed herein. The invention provides a internet hosted system to find, compare, contrast and identify similar characteristics among two or more individuals using a digital camera, cellular telephone camera, wireless device for the purpose of returning information regarding similar faces to the user The system features classification of unknown facial images from a variety of internet accessible sources, including mobile phones, wireless camera-enabled devices, images obtained from digital cameras or scanners that are uploaded from PCs, third-party applications and databases. Once classified, the matching person's name, image and associated meta-data is sent back to the user. The method and system uses human perception techniques to weight the feature vectors.