Open AccessImage Generation Using Bidirectional Integral Features for Face Recognition with a Single Sample per Person
Author(s) -
Yonggeol Lee,
Minsik Lee,
SangIl Choi
Publication year - 2015
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0138859
Subject(s) - artificial intelligence , computer science , pattern recognition (psychology) , face (sociological concept) , facial recognition system , feature (linguistics) , computer vision , image (mathematics) , sample (material) , feature vector , three dimensional face recognition , integral imaging , feature extraction , face detection , physics , social science , linguistics , philosophy , sociology , thermodynamics
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.