Face Recognition System Based on Principal Component Analysis (PCA)

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Resource Overview

A PCA-based face recognition system with 40 sets of facial images. Includes random image selection and recognition demonstration with feature extraction and similarity measurement implementations.

Detailed Documentation

This article presents a face recognition system based on Principal Component Analysis (PCA), accompanied by 40 sets of facial images for reference. The system randomly selects images and performs recognition to demonstrate its effectiveness. Using the PCA algorithm, the system converts facial images into a specific vector space through eigenvalue decomposition of the covariance matrix, and performs recognition by comparing vector similarities using distance metrics like Euclidean distance or cosine similarity. Key implementation steps include data normalization, covariance matrix computation, eigenvector selection for dimensionality reduction, and projection of test images onto the principal component space. This approach not only applies to face recognition but also extends to other domains such as image classification and data compression, making the system highly versatile for various applications.

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