Open AccessLow‐resolution face recognition in uses of multiple‐size discrete cosine transforms and selective Gaussian mixture models
Author(s) -
Huang ShihMing,
Chou YangTing,
Yang JarFerr
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.2012.0211
Subject(s) - facial recognition system , artificial intelligence , computer science , pattern recognition (psychology) , discrete cosine transform , mixture model , face (sociological concept) , gaussian , pixel , computer vision , feature (linguistics) , feature extraction , feature vector , resolution (logic) , image (mathematics) , social science , linguistics , philosophy , physics , quantum mechanics , sociology
Owing to losing the detailed information, the low‐resolution problem in face recognition degrades the recognition performance dramatically. To overcome this problem, a novel face‐recognition system has been proposed, consisting of the extracted feature vectors from the multiple‐size discrete cosine transforms (mDCTs) and the recognition mechanism with selective Gaussian mixture models (sGMMs). The mDCT could extract enough visual features from low‐resolution face images while the sGMM could exclude unreliable observation features in recognition phase. Thus, the mDCT and the sGMM can greatly improve recognition rate at low‐resolution conditions. Experiments are carried out on George Tech and AR facial databases in 16 × 16 and 12 × 12 pixels resolution. The results show that the proposed system achieves better performance than the existing methods for low‐resolution face recognition.