Abstract
This paper describes a face recognition algorithm using feature points of face parts, which is classified as a feature-based method. As recognition performance depends on the combination of extracted feature points, we utilize all reliable feature points effectively. From moving video input, well-conditioned face images with a frontal direction and without facial expression are extracted. To select such well-conditioned images, an iteratively minimizing variance method is used with variable input face images. This iteration drastically brings convergence to the minimum variance of 1 for a quarter to an eighth of all data, which proves to take the frontal image in 0.27 second from video at most. The proposed system using six statistic values realizes 98.3% as an authentication rate.
| Original language | English |
|---|---|
| Pages (from-to) | 29-44 |
| Number of pages | 16 |
| Journal | International Journal of Computer Science and Applications |
| Volume | 13 |
| Issue number | 2 |
| Publication status | Published - 2016 |
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Keywords
- Distance
- Distribution
- Face recognition
- Feature point
ASJC Scopus subject areas
- Computer Science Applications
Cite this
Minimum variance method to obtain the best shot in video and its effectiveness for face recognition. / Ohzeki, Kazuo; Aoyama, Ryota; Hirakawa, Yutaka.
In: International Journal of Computer Science and Applications, Vol. 13, No. 2, 2016, p. 29-44.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Minimum variance method to obtain the best shot in video and its effectiveness for face recognition
AU - Ohzeki, Kazuo
AU - Aoyama, Ryota
AU - Hirakawa, Yutaka
PY - 2016
Y1 - 2016
N2 - This paper describes a face recognition algorithm using feature points of face parts, which is classified as a feature-based method. As recognition performance depends on the combination of extracted feature points, we utilize all reliable feature points effectively. From moving video input, well-conditioned face images with a frontal direction and without facial expression are extracted. To select such well-conditioned images, an iteratively minimizing variance method is used with variable input face images. This iteration drastically brings convergence to the minimum variance of 1 for a quarter to an eighth of all data, which proves to take the frontal image in 0.27 second from video at most. The proposed system using six statistic values realizes 98.3% as an authentication rate.
AB - This paper describes a face recognition algorithm using feature points of face parts, which is classified as a feature-based method. As recognition performance depends on the combination of extracted feature points, we utilize all reliable feature points effectively. From moving video input, well-conditioned face images with a frontal direction and without facial expression are extracted. To select such well-conditioned images, an iteratively minimizing variance method is used with variable input face images. This iteration drastically brings convergence to the minimum variance of 1 for a quarter to an eighth of all data, which proves to take the frontal image in 0.27 second from video at most. The proposed system using six statistic values realizes 98.3% as an authentication rate.
KW - Distance
KW - Distribution
KW - Face recognition
KW - Feature point
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M3 - Article
VL - 13
SP - 29
EP - 44
JO - International Journal of Computer Science and Applications
JF - International Journal of Computer Science and Applications
SN - 0972-9038
IS - 2
ER -