Abstract
In face recognition, most appearance-based methods require several images of each person to construct the feature space for recognition. However, in the real world it is difficult to collect multiple images per person, and in many cases there is only a single sample per person (SSPP). In this paper, we propose a method to generate new images with various illuminations from a single image taken under frontal illumination. Motivated by the integral image, which was developed for face detection, we extract the bidirectional integral feature (BIF) to obtain the characteristics of the illumination condition at the time of the picture being taken. The experimental results for various face databases show that the proposed method results in improved recognition performance under illumination variation.
Highlights
Face recognition is used to identify individuals from facial images by using a face database labeled with people’s identities [1]
One frequently encounters the single sample per person (SSPP) problem, i.e., a situation that only accessibility to a stored SSPP, which is an unfortunate reality underpinned by a number of key issues, including the difficulties associated with collecting samples and storage capability
We propose a novel method to generate new images from a single image to address the SSPP problem
Summary
Face recognition is used to identify individuals from facial images by using a face database labeled with people’s identities [1]. The number of training samples per person continues to exert a major influence on the functioning of appearance-based methods in face recognition, even though the variants of the LDA method, such as PCA+LDA, DCV, and Direct LDA, were considered solutions to the SSS problem. We propose a novel approach to generate new face images from a single training image to solve the SSPP problem. The proposed method is not reliant upon the choice of a particular appearance-based face recognition algorithm, and a single training sample per person is sufficient to improve face recognition performance when there are illumination variations, as shown in the experimental results. We use the proposed method to demonstrate the improvement in face recognition performance, and show how BIF can be used with the virtual face image-generation method
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