Low-resolution face recognition in uses of multiple-size discrete cosine transforms and selective Gaussian mixture models

Shih Ming Huang, Yang Ting Chou, Jar-Ferr Yang

研究成果: Article同行評審

6 引文 斯高帕斯(Scopus)

摘要

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.

原文English
頁(從 - 到)382-390
頁數9
期刊IET Computer Vision
8
發行號5
DOIs
出版狀態Published - 2014 十月 1

All Science Journal Classification (ASJC) codes

  • 軟體
  • 電腦視覺和模式識別

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