Age Group Estimation Based on the Transition Count of 3rd Order Neighborhood using V and Inverted V Patterns
Moka Uma Devi1, Uppu Ravi Babu2
1Moka Uma Devi, PhD, Department Computer Science & Engineering , Acharya Nagarjuna University, Guntur,
2Uppu Ravi Babu, Professor in Department of Computer Science and Engineering in reputed Engineering College.
Manuscript received on November 12, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on 30 November, 2019. | PP: 8792-8796 | Volume-8 Issue-4, November 2019. | Retrieval Number: D9410118419/2019©BEIESP | DOI: 10.35940/ijrte.D9410.118419
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© 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: Age Classification is used in so many applications like crime detection, face detection and so on. . The age leads to significant variation in human face. The variation depends on many factors like gender, exposure to sunlight, drinking, weight loss or weight gain. In our paper the performance of face aging is established based on v pattern and Inverted v pattern by using the transition count of third order neighborhood. In our proposed method the age of the person is divided into 5 categories 1.Childhood (0-12years) 2.Young Adults (13-25years) 3.Middle Age Adults (26-40years) 4.Senior Adults (40-60years) 5.Senior Citizens (more than 60 years).The quantative evaluation and analysis is performed in our proposed method when compared to other existing methods after applying on 4 different facial image databases.
Keywords: V Pattern, Inverted V Pattern, Transition Count, Age Estimation, Third Order Neighborhood.
Scope of the Article: Pattern Recognition and Analysis.