Face Recognition using Curvelet Transform

TL;DR

This paper explores face recognition using Curvelet transform for feature extraction, proposing a new algorithm that outperforms existing methods on multiple face databases with classifiers like k-NN and SVM.

Contribution

It introduces a novel face recognition algorithm leveraging Curvelet transform for improved feature detection and demonstrates its effectiveness across several datasets.

Findings

The proposed algorithm achieves high recognition accuracy.

It outperforms some existing face recognition algorithms.

Effective on multiple face image databases.

Abstract

Face recognition has been studied extensively for more than 20 years now. Since the beginning of 90s the subject has became a major issue. This technology is used in many important real-world applications, such as video surveillance, smart cards, database security, internet and intranet access. This report reviews recent two algorithms for face recognition which take advantage of a relatively new multiscale geometric analysis tool - Curvelet transform, for facial processing and feature extraction. This transform proves to be efficient especially due to its good ability to detect curves and lines, which characterize the human's face. An algorithm which is based on the two algorithms mentioned above is proposed, and its performance is evaluated on three data bases of faces: AT&T (ORL), Essex Grimace and Georgia-Tech. k-nearest neighbour (k-NN) and Support vector machine (SVM) classifiers are used, along with Principal Component Analysis (PCA) for dimensionality reduction. This algorithm shows good results, and it even outperforms other algorithms in some cases.