A Novel Image Description With the Stretched Natural Vector Method: Application to Face Recognition

Wenhui Dong,Stephen S -T Yau

doi:10.1109/access.2020.2997857

Abstract

Nowadays there is growing research interest in designing high performance algorithms for automatic facial recognition systems, and an efficient computational approach is required. Accurate face recognition, however, is difficult due to facial complexity. In this paper, we propose a novel and efficient facial image representation named the Stretched Natural Vector (SNV) method which is defined on the intensity values in a grayscale image matrix, where each entry in an intensity matrix records the level of gray at a single pixel in a m×n array. We prove that the SNV defined in this context can distinguish photo matrices in strict one-to-one fashion. This is to say it is theoretically possible to fully recover a grayscale image matrix from the corresponding complete SNV. Experiments on a number of datasets demonstrate that our truncated SNV method compares favorably both in recognition accuracy and efficiency (measured in wall-clock time) against “Full-Pixel” algorithm, Principal Component Analysis (PCA) method, and even its widely used variants - two dimensional PCA (2DPCA) method and two dimensional Euler PCA (2D-EPCA) method.

Highlights

Face recognition is one of the most important methods for biometrical recognition
A novel approach coined the Stretched Natural Vector (SNV) method is proposed for image representation, which is based on two-dimensional grayscale image matrix directly
THE APPLICATION OF THE SNV METHOD IN FACE RECOGNITION The proposed SNV method is used for face recognition and tested on three datasets (ORL dataset, ‘total_73_95faces’ dataset, and ‘total_151_96faces’ dataset)

Summary

INTRODUCTION

Face recognition is one of the most important methods for biometrical recognition. It has wide applications, including criminal identification, security systems, image and film processing, and human-computer interaction. Combining the advantages of 2DPCA with EPCA, researchers in [27] introduce a two-dimensional Euler PCA (2D-EPCA) algorithm, which leans projection matrix on the 2D pixel matrix of each image without reshaping it into 1D long vector, and uncovers nonlinear relationships among features by mapping data onto complex representation. A novel approach coined the Stretched Natural Vector (SNV) method is proposed for image representation, which is based on two-dimensional grayscale image matrix directly. We use a truncated SNV method to match face images to people in order to compute recognition accuracy and time consuming, comparing with the basic PCA method, its variants–2DPCA and 2D-EPCA methods and the ‘‘Full-Pixel’’ algorithm, which is detailed in (18).

CONSTRUCTION OF THE STRETCHED NATURAL

THE APPLICATION OF THE SNV METHOD IN FACE RECOGNITION

EXPERIMENTS ON THE ORL DATASET

Findings

CONCLUSIONS