Open AccessFace recognition based on fusion of SVD and Legendre moment
Author(s) -
Asaad Noori Hashimi,
Buraq Noaman Kadhim
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1530/1/012120
Subject(s) - computer science , artificial intelligence , facial recognition system , singular value decomposition , biometrics , face (sociological concept) , pattern recognition (psychology) , three dimensional face recognition , feature extraction , field (mathematics) , moment (physics) , noise (video) , computer vision , identification (biology) , word error rate , face detection , image (mathematics) , mathematics , social science , physics , classical mechanics , sociology , pure mathematics , botany , biology
Face recognition system may be defined as identifying the human face from scenes of video or a static digital image using a computer application. Human face recognition has been gained huge attention as a result of the significant role in various applications such as security, medical applications, forensic evidence, etc. Human face recognition has been faced by many challenges such as illuminations, rotation, noise, blure, etc. Each challenge may need special handling, hence, the face identification and face verification considered as a difficult field. Several algorithms were applied, each one has weaknesses and strengths, these algorithms focus on the pre-processing stage or features extraction or features selections or classification or it may be focuses on all these steps. This paper suggested a new way based on the fusion of features resulted from applying a common known moment, Legendre with a vector of features produced by a applying singular value decomposition transform (SVD), The system has been tested on FEI Brazil database and achieved recognition rate from 95% to 100%, also the suggested algorithm applied on ORL Databases and achieved recognition rate exceed to 98%. Finally, the proposed method performed a higher recognition rate under uncontrolled environment and noisy and blur images.