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

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)382-390
Number of pages9
JournalIET Computer Vision
Volume8
Issue number5
DOIs
Publication statusPublished - 2014 Oct 1

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

Fingerprint

Dive into the research topics of 'Low-resolution face recognition in uses of multiple-size discrete cosine transforms and selective Gaussian mixture models'. Together they form a unique fingerprint.

Cite this