Parallel AdaBoost Algorithm for Gabor Wavelet Selection in Face Recognition

TL;DR

This paper introduces a parallel AdaBoost algorithm incorporating mutual information for automatic Gabor wavelet selection, significantly improving face recognition accuracy and efficiency with reduced computational costs.

Contribution

The paper presents a novel parallel AdaBoosting method with mutual information for optimized Gabor wavelet selection in face recognition.

Findings

High recognition rates achieved on FERET database

Significant reduction in memory and computation costs

Improved classification accuracy with optimized feature selection

Abstract

In this paper, the problem of automatic Gabor wavelet selection for face recognition is tackled by introducing an automatic algorithm based on Parallel AdaBoosting method. Incorporating mutual information into the algorithm leads to the selection procedure not only based on classification accuracy but also on efficiency. Effective image features are selected by using properly chosen Gabor wavelets optimised with Parallel AdaBoost method and mutual information to get high recognition rates with low computational cost. Experiments are conducted using the well-known FERET face database. In proposed framework, memory and computation costs are reduced significantly and high classification accuracy is obtained.