DBC based Face Recognition using DWT

TL;DR

This paper introduces a face recognition method combining DWT and DBC features, achieving improved recognition accuracy on NIR face images by extracting detailed features from face regions.

Contribution

The paper proposes a novel face recognition approach using DWT and DBC features, enhancing recognition performance over existing algorithms.

Findings

Higher recognition accuracy than existing methods

Effective feature extraction from face images

Robustness on NIR face database

Abstract

The applications using face biometric has proved its reliability in last decade. In this paper, we propose DBC based Face Recognition using DWT (DBC- FR) model. The Poly-U Near Infra Red (NIR) database images are scanned and cropped to get only the face part in pre-processing. The face part is resized to 100*100 and DWT is applied to derive LL, LH, HL and HH subbands. The LL subband of size 50*50 is converted into 100 cells with 5*5 dimention of each cell. The Directional Binary Code (DBC) is applied on each 5*5 cell to derive 100 features. The Euclidian distance measure is used to compare the features of test image and database images. The proposed algorithm render better percentage recognition rate compared to the existing algorithm.