Original and Mirror Face Images and Minimum Squared Error Classification for Visible Light Face Recognition.

Rong Wang

doi:10.1155/2015/842084

Abstract

In real-world applications, the image of faces varies with illumination, facial expression, and poses. It seems that more training samples are able to reveal possible images of the faces. Though minimum squared error classification (MSEC) is a widely used method, its applications on face recognition usually suffer from the problem of a limited number of training samples. In this paper, we improve MSEC by using the mirror faces as virtual training samples. We obtained the mirror faces generated from original training samples and put these two kinds of samples into a new set. The face recognition experiments show that our method does obtain high accuracy performance in classification.

Highlights

The conventional minimum squared error (MSE) algorithm has been widely used for pattern recognition and this algorithm performs well in face classification
The well-known representation based methods can be viewed as generalized minimum squared error classification (MSEC) methods, for example, collaborative representation classification (CRC) [24], twophase test samples sparse representation (TPTSSR) [25], and sparse representation-based classifier (SRC) [26]
Our method proposed the MSE classification based on mirror faces [44, 45] for face recognition

Summary

Introduction

The conventional minimum squared error (MSE) algorithm has been widely used for pattern recognition and this algorithm performs well in face classification. The solutions of illumination invariable face recognition [33,34,35,36] can be classified into three kinds, method based on the normal features, modeling based on changed illumination, and having a standard condition for illumination It seems that more training samples are able to reveal more possible variations of the illumination, facial expression, and poses and beneficial for correct classification of the face. In order to obtain better face classification, previous literatures have proposed synthesizing new samples from the true face image. Tang et al [39] proposed prototype faces and an optic flow and expression ratio image based method to generate virtual samples. Our method proposed the MSE classification based on mirror faces [44, 45] for face recognition.

The Proposed Method

Analysis of the Proposed Method

Experimental Results

Conclusions