Open AccessIllumination normalisation using convolutional neural network with application to face recognition
Author(s) -
Kim Y.H.,
Kim H.,
Kim S.W.,
Kim H.Y.,
Ko S.J.
Publication year - 2017
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2017.0023
Subject(s) - artificial intelligence , convolutional neural network , computer science , pattern recognition (psychology) , discriminative model , facial recognition system , face (sociological concept) , pixel , computer vision , feature extraction , classifier (uml) , artificial neural network , shadow (psychology) , feature (linguistics) , psychology , social science , linguistics , philosophy , sociology , psychotherapist
A novel illumination normalisation (IN) method using a convolutional neural network (CNN) is proposed. The proposed network is composed of the local pattern extraction (LPE) and illumination elimination (IE) layers. The LPE layers model the relationships between the pixels in each local region in order to handle various types of local shadow and shading in the face image. Based on the commonly used assumption about the illumination field, the IE layers generate illumination‐insensitive ratio images by calculating the ratio between the output pairs produced from the LPE layers. The final feature map obtained by combining the ratio images can possess an improved discriminative ability for face recognition (FR). For training the proposed network, the results produced by the Weber fraction‐based IN methods as ground truths are utilised. The experimental results demonstrate that the proposed network performs better in terms of FR accuracy compared with the conventional non‐CNN‐based method and it can be combined with any CNN‐based face classifier.