TY - GEN
T1 - Fuzzy-appearance manifold and fuzzy nearest distance for face recognition on various poses and degraded images
AU - Nugroho, Muhammad Adi
AU - Putro, Benyamin Kusumo
PY - 2017/12/5
Y1 - 2017/12/5
N2 - This paper introduces an approach to recognize face from 3D space on 2D image using fuzzy vector manifolds and nearest distance. We employ fuzzy vector to help the system minimize negative effect coming from noise and image degradation. On the training set, crisp vector representation of images will be transformed to its fuzzy vector representation using a specific triangle fuzzification method. Then, a linear interpolation method will be used to construct a manifold, making the system able to cope with pose variation across data. In the testing phase, we transform every unknown data image to its fuzzy-vector representation using the parameter we obtained from training phase. We then project the unknown fuzzy vector to the manifolds using a technique called fuzzy nearest distance. The output of the system will be the index of manifold that the data mostly belong to, in this case the prediction of person. This system is applied to recognize photos on our databases which some of them are influenced by noises. Experiment result show that the system is able to recognize person on 98% success rate, with a 3% reduction if noises were added.
AB - This paper introduces an approach to recognize face from 3D space on 2D image using fuzzy vector manifolds and nearest distance. We employ fuzzy vector to help the system minimize negative effect coming from noise and image degradation. On the training set, crisp vector representation of images will be transformed to its fuzzy vector representation using a specific triangle fuzzification method. Then, a linear interpolation method will be used to construct a manifold, making the system able to cope with pose variation across data. In the testing phase, we transform every unknown data image to its fuzzy-vector representation using the parameter we obtained from training phase. We then project the unknown fuzzy vector to the manifolds using a technique called fuzzy nearest distance. The output of the system will be the index of manifold that the data mostly belong to, in this case the prediction of person. This system is applied to recognize photos on our databases which some of them are influenced by noises. Experiment result show that the system is able to recognize person on 98% success rate, with a 3% reduction if noises were added.
KW - face recognition
KW - fuzzy line interpolation
KW - fuzzy manifold
KW - fuzzy nearest distance
KW - fuzzy vector
KW - image noises
UR - http://www.scopus.com/inward/record.url?scp=85045900261&partnerID=8YFLogxK
U2 - 10.1109/QIR.2017.8168508
DO - 10.1109/QIR.2017.8168508
M3 - Conference contribution
AN - SCOPUS:85045900261
T3 - QiR 2017 - 2017 15th International Conference on Quality in Research (QiR): International Symposium on Electrical and Computer Engineering
SP - 342
EP - 346
BT - QiR 2017 - 2017 15th International Conference on Quality in Research (QiR)
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th International Conference on Quality in Research: International Symposium on Electrical and Computer Engineering, QiR 2017
Y2 - 24 July 2017 through 27 July 2017
ER -