A Face Recognition Scheme using Wavelet Based Dominant Features

TL;DR

This paper introduces a multi-resolution face recognition method using wavelet transform and entropy-based feature selection, achieving high accuracy by extracting dominant local features and reducing dimensionality.

Contribution

It proposes a novel feature extraction approach combining 2D-DWT with entropy-based local band selection and dominant coefficient identification for improved face recognition.

Findings

Achieves high recognition accuracy on standard face databases.

Reduces feature dimension significantly while maintaining discriminative power.

Demonstrates superiority over existing face recognition methods.

Abstract

In this paper, a multi-resolution feature extraction algorithm for face recognition is proposed based on two-dimensional discrete wavelet transform (2D-DWT), which efficiently exploits the local spatial variations in a face image. For the purpose of feature extraction, instead of considering the entire face image, an entropy-based local band selection criterion is developed, which selects high-informative horizontal segments from the face image. In order to capture the local spatial variations within these highinformative horizontal bands precisely, the horizontal band is segmented into several small spatial modules. Dominant wavelet coefficients corresponding to each local region residing inside those horizontal bands are selected as features. In the selection of the dominant coefficients, a threshold criterion is proposed, which not only drastically reduces the feature dimension but also provides high within-class compactness and high between-class separability. A principal component analysis is performed to further reduce the dimensionality of the feature space. Extensive experimentation is carried out upon standard face databases and a very high degree of recognition accuracy is achieved by the proposed method in comparison to those obtained by some of the existing methods.