Open AccessFace recognition based on the fusion of global and local HOG features of face images
Author(s) -
Tan Hengliang,
Yang Bing,
Ma Zhengming
Publication year - 2014
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2012.0302
Subject(s) - artificial intelligence , pattern recognition (psychology) , histogram , facial recognition system , computer science , histogram of oriented gradients , classifier (uml) , three dimensional face recognition , face (sociological concept) , local binary patterns , gabor filter , feature extraction , computer vision , feature (linguistics) , face detection , image (mathematics) , social science , linguistics , philosophy , sociology
Histogram of oriented gradients (HOG) descriptor was initially applied to human detection and achieved great success. In recent years, HOG descriptor has also been applied to face recognition. However, comparing with other sophisticated feature descriptors such as LBP, Gabor and so on, there are still considerable research space on the application of HOG features for face recognition. There are two main contributions. On one hand, the main parameters are statistically analysed characterising HOG descriptor for face recognition, which seems to be not discussed clearly in literatures so far. On the other hand, a novel framework for face recognition based on the fusion of global and local HOG features has been proposed. Face images are first illumination normalised by the DoG filter. Secondly, global and local HOG features are extracted by PCA + LDA or LDA with different framework. Finally, in decision level, global and local classifiers are built by the nearest neighbour classifier, after that, two classifiers are fused by a weighted sum rule. Experimental results on two large‐scale face databases FERET and CAS‐PEAL‐R1 show that, in comparison with 12 state‐of‐the‐art approaches of face recognition, the proposed method achieves the highest average recognition rate.