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 language | English |
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Pages (from-to) | 382-390 |
Number of pages | 9 |
Journal | IET Computer Vision |
Volume | 8 |
Issue number | 5 |
DOIs | |
Publication status | Published - 2014 Oct 1 |
All Science Journal Classification (ASJC) codes
- Software
- Computer Vision and Pattern Recognition