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General information |
| Author |
Kusumoputro, Benyamin; Lina |
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InTech Open Access Publisher, 2013
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| Abstract |
This paper presents the development of fuzzyappearance
manifold and fuzzy‐nearest distance
calculation in the eigenspace domain for pose estimation
of degraded face images. In order to obtain a robust pose
estimation system which can deal with the fuzziness of
face data caused by statistical errors, we proposed the
fuzzy‐vector representation in eigenspace domain of the
face images. Using fuzzy‐vector representations, all of the
crisp vectors of face data in the eigenspace domain are
firstly transformed into fuzzy‐vectors as fuzzy‐points.
Next, the fuzzy‐appearance manifold is constructed from
all the available fuzzy‐points and the fuzzy‐nearest
distance calculation is proposed as the classifier of the
pose estimation system. The pose estimation of an
unknown face image is performed by firstly being
projected onto the eigenspace domain then transformed
to become an unknown fuzzy‐point, and its fuzzydistance
with all of the available fuzzy‐points in the
fuzzy‐appearance manifold will be calculated. The fuzzypoint
in the manifold which has the nearest distance to
that unknown fuzzy‐point will be determined as the pose
position of the unknown face image. In the experiment,
face images with various quality degradation effects were
used. The results show that the system could maintain
high recognition rates for estimating the pose position of
the degraded face images. |
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International Journal of Advanced Robotic Systems
Author: Ottaviano, Erika; Ceccarelli, Marco; Husty, Manfred; Yu, Sung-Hoon; Kim, Yong-Tae; Park, Chang-Woo; Hyun, Chang-Ho; Chen, Xiulong; Feng, Weiming; Sun, Xianyang; Gao, Qing; Grigorescu, Sorin M.; Pozna, Claudiu; Liu, Wanli; Zhankui, Wang; Guo, Meng; Fu, Guoyu; Zhang, Jin; Chen, Wenyuan; Peng, Fengchao; Yang, Pei; Chen, Chunlin; Ding, Rui; Yu, Junzhi; Yang, Qinghai; Tan, Min; Polden, Joseph; Pan, [...]
Published: 2004
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