Face Recognition Based on SVM and 2DPCA

TL;DR

This paper introduces a face recognition method combining 2DPCA for feature extraction and SVM for classification, demonstrating improved accuracy on public datasets.

Contribution

It presents a novel integration of 2DPCA and SVM for face recognition, enhancing classification performance.

Findings

Improved classification rates on FERET and AT&T datasets.

Effective combination of 2DPCA and SVM for face recognition.

Validated approach with experimental results.

Abstract

The paper will present a novel approach for solving face recognition problem. Our method combines 2D Principal Component Analysis (2DPCA), one of the prominent methods for extracting feature vectors, and Support Vector Machine (SVM), the most powerful discriminative method for classification. Experiments based on proposed method have been conducted on two public data sets FERET and AT&T; the results show that the proposed method could improve the classification rates.