ANN Classifier for Human Age Classification | Zendy

R. Thanuja | Zendy; Dr.A.Umama keswari | Zendy; D. Rubidha Devi | Zendy

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ANN Classifier for Human Age Classification

Author(s) -

R. Thanuja,

Dr.A.Umama keswari,

D. Rubidha Devi

Publication year - 2019

Publication title -

international journal of recent technology and engineering (ijrte)

Language(s) - English

Resource type - Journals

ISSN - 2277-3878

DOI - 10.35940/ijrte.c5430.098319

Subject(s) - leaps , computer science , artificial intelligence , local binary patterns , biometrics , classifier (uml) , pattern recognition (psychology) , face (sociological concept) , facial recognition system , artificial neural network , wrinkle , binary classification , machine learning , support vector machine , image (mathematics) , medicine , social science , sociology , financial economics , economics , histogram , gerontology

Face recognition is an interesting research study with many researchers from computer vision and biometrics fields.The performance of existing methods on real-world images is still significantly lacking, especially when compared to the tremendous leaps in performance recently reported for the related task of face recognition. In this paper we propose a novel technique to group the age of a human dependent on facial skin maturing highlights. Artificial Neural Network (ANN) is proposed to characterize human age into different age gatherings. The ideal highlights are removed utilizing advanced picture handling methods like Local Binary Pattern (LBP), Elliptical Local Binary Pattern (ELBP) and Wrinkle Analysis. The proposed age characterization structure is prepared to test with human face pictures from SQL database with high precision.

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