ANN Classifier for Human Age Classification
Thanuja R1, Umamakeswari A2, Rubidha Devi D3
1Thanuja R, Dept. of CSE, SASTRA DEEMED UNIVERSITY, Thanjavur India.
2Dr.A.Umamakeswari, Dept. of CSE, SASTRA DEEMED UNIVERSITY, Thanjavur India.
3RubidhaDevi D, Dept. of CSE, SASTRA DEEMED UNIVERSITY, Thanjavur India.
Manuscript received on 03 August 2019. | Revised Manuscript received on 08 August 2019. | Manuscript published on 30 September 2019. | PP: 5827-5830 | Volume-8 Issue-3 September 2019 | Retrieval Number: C5430098319/2019©BEIESP | DOI: 10.35940/ijrte.C5430.098319
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Local Binary Pattern (Lbp); Elliptical Local Binary Pattern (Elbp); Wrinkle Analysis; Age Classification.
Scope of the Article: Classification