TY - GEN
T1 - Development of fuzzy manifold and fuzzy nearest distance for pose estimation of degraded face images
AU - Putro, Benyamin Kusumo
AU - Kresnaraman, Brahmastro
AU - Lina,
PY - 2010/12/1
Y1 - 2010/12/1
N2 - This paper presents a novel approach to estimate the pose position of the degraded face images using fuzzy vector representation. We focus on the development of fuzzy-manifold as an interpolation line between all the available learning data and the fuzzy distance calculation as the classifier. Crisp-based vector of the face image is firstly transformed into fuzzy-based vector, to deal with the fuzziness of the data caused by statistical measurement error directly. Two types of fuzzy interpolation methods are used to construct the fuzzy-manifold. Estimating the pose position of an unknown crisp-image vector is accomplished by firstly transformed into a fuzzy-vector and calculates the nearest fuzzy-distances to all available fuzzy-points in the designated fuzzy-lines. This system is then applied to estimate a degraded face images influenced by quality degradation effects. Experiment results show that those degradation effects due to various noise additions did not decrease the recognition rate.
AB - This paper presents a novel approach to estimate the pose position of the degraded face images using fuzzy vector representation. We focus on the development of fuzzy-manifold as an interpolation line between all the available learning data and the fuzzy distance calculation as the classifier. Crisp-based vector of the face image is firstly transformed into fuzzy-based vector, to deal with the fuzziness of the data caused by statistical measurement error directly. Two types of fuzzy interpolation methods are used to construct the fuzzy-manifold. Estimating the pose position of an unknown crisp-image vector is accomplished by firstly transformed into a fuzzy-vector and calculates the nearest fuzzy-distances to all available fuzzy-points in the designated fuzzy-lines. This system is then applied to estimate a degraded face images influenced by quality degradation effects. Experiment results show that those degradation effects due to various noise additions did not decrease the recognition rate.
KW - Fuzzy distance calculation
KW - Fuzzy line interpolation
KW - Fuzzy number
KW - Fuzzy vector
UR - http://www.scopus.com/inward/record.url?scp=79952587618&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:79952587618
SN - 9789604741663
T3 - Proceedings of the 9th WSEAS International Conference on Applications of Computer Engineering, ACE '10
SP - 180
EP - 185
BT - Proceedings of the 9th WSEAS International Conference on Applications of Computer Engineering, ACE '10
T2 - 9th WSEAS International Conference on Applications of Computer Engineering, ACE '10
Y2 - 23 March 2010 through 25 March 2010
ER -