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

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.