Training Set Enlargement Using Binary Weighted Interpolation Maps for the Single Sample per Person Problem in Face Recognition

Yonggeol Lee,Sang-Il Choi

doi:10.3390/app10196659

Yonggeol Lee, Sang-Il Choi

Open Access

https://doi.org/10.3390/app10196659

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Abstract

We propose a method of enlarging the training dataset for a single-sample-per-person (SSPP) face recognition problem. The appearance of the human face varies greatly, owing to various intrinsic and extrinsic factors. In order to build a face recognition system that can operate robustly in an uncontrolled, real environment, it is necessary for the algorithm to learn various images of the same person. However, owing to limitations in the collection of facial image data, only one sample can typically be obtained, causing difficulties in the performance and usability of the method. This paper proposes a method that analyzes the changes in pixels in face images associated with variations by extracting the binary weighted interpolation map (B-WIM) from neutral and variational images in the auxiliary set. Then, a new variational image for the query image is created by combining the given query (neutral) image and the variational image of the auxiliary set based on the B-WIM. As a result of performing facial recognition comparison experiments on SSPP training data for various facial-image databases, the proposed method shows superior performance compared with other methods.

Highlights

Face recognition technology is used to identify individuals from their captured facial images by leveraging a labeled database containing people’s identities
We propose binary weighted interpolation maps (B-WIM) to enlarge the training set for face recognition
We compare the proposed method with other methods dealing with the SSPP problem: WIM, interclass relationship (ICR), E(PC2 )A+, SPCA+, (2D)2 principal component analysis (PCA), SLC, MVI, and SRGES

Summary

Introduction

Face recognition technology is used to identify individuals from their captured facial images by leveraging a labeled database containing people’s identities. Because variations caused by extrinsic factors (e.g., illumination and pose) and intrinsic factors (e.g., facial expression, age, and accessories) are very large, it is difficult to robustly recognize a face under uncontrolled conditions [1,2]. To deal with these variations, facial recognition methods have been studied under the assumption that several images can be made available for each person, and high-performance methods have been built using vast databases of this nature (e.g., VGGface2 [3], Tufts Face [4], UMDfaces [5]), MegaFace [6], and LFW [7,8] databases).

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