Low Resolution Face Recognition by Using Variable Block DCT and Selective Likelihood GMM

• Main Authors: Yang-TingChou, 周暘庭
• Other Authors: Jar-Ferr Yang
• Format: Others
• Language: en_US
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/72985187521051226601

碩士 === 國立成功大學 === 電腦與通信工程研究所 === 100 === The low resolution problem in face recognition, which often occurs in video surveillance applications, degrades the detection performance dramatically. To overcome the low resolution problem, in this thesis, we propose a novel face recognition system, which collects the observation vectors extracted from variable block discrete cosine transform (VB_DCT) and recognizes the identify by using selective likelihood Gaussian mixture modeling (SL_GMM). The VB_DCT successfully extends the observation vectors from small to global views of low resolution faces while the SL_GMM greatly helps to exclude insignificant local features during the recognition phase to improve the detection performance significantly. Experimental results, which were carried out on the ORL database and the AR database in size of 12×12 pixels after subsampling, show that the proposed method achieves better performance for low resolution face recognition, even under partial occlusion.