IEICE TRANSACTIONS on Information
Class-Distance-Based Discriminant Analysis and Its Application to Supervised Automatic Age Estimation
Summary :
We propose a novel method of supervised feature projection called class-distance-based discriminant analysis (CDDA), which is suitable for automatic age estimation (AAE) from facial images. Most methods of supervised feature projection, e.g., Fisher discriminant analysis (FDA) and local Fisher discriminant analysis (LFDA), focus on determining whether two samples belong to the same class (i.e., the same age in AAE) or not. Even if an estimated age is not consistent with the correct age in AAE systems, i.e., the AAE system induces error, smaller errors are better. To treat such characteristics in AAE, CDDA determines between-class separability according to the class distance (i.e., difference in ages); two samples with similar ages are imposed to be close and those with spaced ages are imposed to be far apart. Furthermore, we propose an extension of CDDA called local CDDA (LCDDA), which aims at handling multimodality in samples. Experimental results revealed that CDDA and LCDDA could extract more discriminative features than FDA and LFDA.
- Publication
- IEICE TRANSACTIONS on Information Vol.E94-D No.8 pp.1683-1689
- Publication Date
- 2011/08/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E94.D.1683
- Type of Manuscript
- PAPER
- Category
- Image Recognition, Computer Vision
Authors
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Tetsuji OGAWA, Kazuya UEKI, Tetsunori KOBAYASHI, "Class-Distance-Based Discriminant Analysis and Its Application to Supervised Automatic Age Estimation" in IEICE TRANSACTIONS on Information,
vol. E94-D, no. 8, pp. 1683-1689, August 2011, doi: 10.1587/transinf.E94.D.1683.
Abstract: We propose a novel method of supervised feature projection called class-distance-based discriminant analysis (CDDA), which is suitable for automatic age estimation (AAE) from facial images. Most methods of supervised feature projection, e.g., Fisher discriminant analysis (FDA) and local Fisher discriminant analysis (LFDA), focus on determining whether two samples belong to the same class (i.e., the same age in AAE) or not. Even if an estimated age is not consistent with the correct age in AAE systems, i.e., the AAE system induces error, smaller errors are better. To treat such characteristics in AAE, CDDA determines between-class separability according to the class distance (i.e., difference in ages); two samples with similar ages are imposed to be close and those with spaced ages are imposed to be far apart. Furthermore, we propose an extension of CDDA called local CDDA (LCDDA), which aims at handling multimodality in samples. Experimental results revealed that CDDA and LCDDA could extract more discriminative features than FDA and LFDA.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E94.D.1683/_p
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@ARTICLE{e94-d_8_1683,
author={Tetsuji OGAWA, Kazuya UEKI, Tetsunori KOBAYASHI, },
journal={IEICE TRANSACTIONS on Information},
title={Class-Distance-Based Discriminant Analysis and Its Application to Supervised Automatic Age Estimation},
year={2011},
volume={E94-D},
number={8},
pages={1683-1689},
abstract={We propose a novel method of supervised feature projection called class-distance-based discriminant analysis (CDDA), which is suitable for automatic age estimation (AAE) from facial images. Most methods of supervised feature projection, e.g., Fisher discriminant analysis (FDA) and local Fisher discriminant analysis (LFDA), focus on determining whether two samples belong to the same class (i.e., the same age in AAE) or not. Even if an estimated age is not consistent with the correct age in AAE systems, i.e., the AAE system induces error, smaller errors are better. To treat such characteristics in AAE, CDDA determines between-class separability according to the class distance (i.e., difference in ages); two samples with similar ages are imposed to be close and those with spaced ages are imposed to be far apart. Furthermore, we propose an extension of CDDA called local CDDA (LCDDA), which aims at handling multimodality in samples. Experimental results revealed that CDDA and LCDDA could extract more discriminative features than FDA and LFDA.},
keywords={},
doi={10.1587/transinf.E94.D.1683},
ISSN={1745-1361},
month={August},}
Copy
TY - JOUR
TI - Class-Distance-Based Discriminant Analysis and Its Application to Supervised Automatic Age Estimation
T2 - IEICE TRANSACTIONS on Information
SP - 1683
EP - 1689
AU - Tetsuji OGAWA
AU - Kazuya UEKI
AU - Tetsunori KOBAYASHI
PY - 2011
DO - 10.1587/transinf.E94.D.1683
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E94-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2011
AB - We propose a novel method of supervised feature projection called class-distance-based discriminant analysis (CDDA), which is suitable for automatic age estimation (AAE) from facial images. Most methods of supervised feature projection, e.g., Fisher discriminant analysis (FDA) and local Fisher discriminant analysis (LFDA), focus on determining whether two samples belong to the same class (i.e., the same age in AAE) or not. Even if an estimated age is not consistent with the correct age in AAE systems, i.e., the AAE system induces error, smaller errors are better. To treat such characteristics in AAE, CDDA determines between-class separability according to the class distance (i.e., difference in ages); two samples with similar ages are imposed to be close and those with spaced ages are imposed to be far apart. Furthermore, we propose an extension of CDDA called local CDDA (LCDDA), which aims at handling multimodality in samples. Experimental results revealed that CDDA and LCDDA could extract more discriminative features than FDA and LFDA.
ER -
English
