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 |
|---|---|
| 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 |
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All Science Journal Classification (ASJC) codes
- Software
- Computer Vision and Pattern Recognition
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Low-resolution face recognition in uses of multiple-size discrete cosine transforms and selective Gaussian mixture models. / Huang, Shih Ming; Chou, Yang Ting; Yang, Jar-Ferr.
In: IET Computer Vision, Vol. 8, No. 5, 01.10.2014, p. 382-390.Research output: Contribution to journal › Article
TY - JOUR
T1 - Low-resolution face recognition in uses of multiple-size discrete cosine transforms and selective Gaussian mixture models
AU - Huang, Shih Ming
AU - Chou, Yang Ting
AU - Yang, Jar-Ferr
PY - 2014/10/1
Y1 - 2014/10/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84907160769&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84907160769&partnerID=8YFLogxK
U2 - 10.1049/iet-cvi.2012.0211
DO - 10.1049/iet-cvi.2012.0211
M3 - Article
VL - 8
SP - 382
EP - 390
JO - IET Computer Vision
JF - IET Computer Vision
SN - 1751-9632
IS - 5
ER -