Fusing Two Kinds of Virtual Samples for Small Sample Face Recognition

Minna Qiu,Jiayan Yang,Jian Zhang,Liying Ye

doi:10.1155/2015/280318

Minna Qiu, Jiayan Yang + Show 2 more

Open Access

https://doi.org/10.1155/2015/280318

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Abstract

Face recognition has become a very active field of biometrics. Different pictures of the same face might include various changes of expressions, poses, and illumination. However, a face recognition system usually suffers from the problem that nonsufficient training samples cannot convey these possible changes effectively. The main reason is that a system has only limited storage space and limited time to capture training samples. Many previous literatures ignored the problem of nonsufficient training samples. In this paper, we overcome the insufficiency of training sample size problem by fusing two kinds of virtual samples and the original samples to perform small sample face recognition. The two used kinds of virtual samples are mirror faces and symmetrical faces. Firstly, we transform the original face image to obtain mirror faces and symmetrical faces. Secondly, we fuse these two kinds of virtual samples to achieve the matching scores between the test sample and each class. Finally, we integrate the matching scores to get the final classification results. We compare the proposed method with the single virtual sample augment methods and the original representation-based classification. The experiments on various face databases show that the proposed scheme achieves the best accuracy among the representation-based classification methods.

Highlights

Face recognition [1,2,3,4,5] is paid more and more attention as a branch of biometrics [6,7,8,9]
We use the collaborative representation based classification (CRC) method to do the experiment in ORL, Yale, FERET, and FLW databases
The reasons why the COVNFR method cannot achieve a satisfying result are the following: if we use the virtual training samples only to do face recognition, all of the classification rates in Algorithm 1 are higher than 90% which means the virtual training sample cannot inherit all of the features

Summary

Introduction

Face recognition [1,2,3,4,5] is paid more and more attention as a branch of biometrics [6,7,8,9]. In [22] and [23], Xu et al aimed at solving nonsymmetrical samples and misalignment problem They proposed a method which exploits the mirror image of the face image to simulate possible variation. The proposed method integrates the original training sample and its virtual samples to perform face recognition. The proposed scheme is not a simple combination of all the virtual samples and training samples, but a fusion of weights among the original training samples, the mirror virtual faces, and the symmetrical virtual faces. (2) The proposed method is not based on a single combination of all the virtual samples and training samples but is based on a weighted fusion of the original training samples, the mirror virtual faces, and the symmetrical virtual faces.

Related Work

The Proposed Scheme

Experimental Results

Conclusion