Facial Recognition Feature Vector Matching via Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current facial recognition technologies are limited in providing a simple and inexpensive means for the general public to determine which celebrity a person resembles, especially through digital images captured by wireless devices or personal computers.
Innovation Solution
A method and system that classifies digital facial images by processing them into feature vectors, comparing these vectors against a database of celebrity feature vectors, and returning the closest match along with associated metadata, allowing users to view the results on their devices or through a website.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If facial recognition software is used to identify celebrities from digital images, then identification accuracy is improved, but system complexity and cost increase
Solution Approach 1:
The system segments the facial recognition process into distinct modules: face detection component, feature extraction component, and celebrity matching component. Each module performs a specific function, allowing the complex system to be managed through modular components that can be independently optimized and maintained.
Solution Approach 2:
The system introduces an intermediary database containing pre-stored celebrity facial images and特征 vectors. This intermediary layer enables comparison between user-uploaded images and the celebrity database without requiring direct complex processing between all possible image pairs, thus reducing computational complexity while maintaining accuracy.
2Measurement precision
If detailed facial feature analysis is performed to improve match accuracy, then measurement precision is improved, but processing time increases
Solution Approach 1:
The system performs preliminary action by pre-processing celebrity images into standardized feature vectors and storing them in the database before actual matching occurs. When a user uploads an image, the system only needs to extract features from the user image and compare against pre-computed celebrity features, significantly reducing processing time while maintaining detailed analysis accuracy.
Solution Approach 2:
The system applies local quality by focusing detailed analysis only on relevant facial regions (eyes, nose, mouth, jawline) rather than processing the entire image uniformly. This selective feature extraction maintains match accuracy by concentrating computational resources on discriminative facial features while reducing overall processing time.
3Reliability
If the system processes and stores large numbers of facial images in a database, then identification reliability is improved, but data storage requirements and system resources increase
Solution Approach 1:
The system uses copying by storing compressed feature vector representations of facial images rather than the full-resolution original images. Each celebrity's facial data is converted into a compact numerical feature vector that captures essential facial characteristics, enabling reliable identification while dramatically reducing storage requirements compared to storing complete high-resolution images.
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.