Optimizing Face Recognition Using PCA

TL;DR

This paper presents an optimized PCA-based face recognition method that reduces computational time by 35% without sacrificing recognition accuracy, tested on the Face94 database.

Contribution

It introduces a technique to minimize eigenvectors in PCA, significantly decreasing recognition time while maintaining performance.

Findings

Recognition time reduced by 35%

Performance remains unaffected by optimization

Effective on Face94 database

Abstract

Principle Component Analysis PCA is a classical feature extraction and data representation technique widely used in pattern recognition. It is one of the most successful techniques in face recognition. But it has drawback of high computational especially for big size database. This paper conducts a study to optimize the time complexity of PCA (eigenfaces) that does not affects the recognition performance. The authors minimize the participated eigenvectors which consequently decreases the computational time. A comparison is done to compare the differences between the recognition time in the original algorithm and in the enhanced algorithm. The performance of the original and the enhanced proposed algorithm is tested on face94 face database. Experimental results show that the recognition time is reduced by 35% by applying our proposed enhanced algorithm. DET Curves are used to illustrate the experimental results.