Rajendra Babu .Ch

Work place: SRK Institute of Technology, Vijayawada, 520008, India

E-mail: chikkalarajendra@gmail.com


Research Interests: Image Compression, Image Manipulation, Image Processing


Rajendra Babu Ch: Received the B.Tech degree in Computer Science & Engineering from Jawaharlal Nehru Technological University, Hyderabad, India in 2005, M.Tech. degree in Computer Science and Engineering from Acharya Nagarjuna University, India in 2008, and registered for Ph.D. in Computer Science and Engineering at Jawaharlal Nehru Technological University under the guidance of Prof. E. Srinivasa Reddy and Prof. B.Prabhakara Rao. His research interests include Image Processing.

Author Articles
Novel Approach for Child and Adulthood Classification Based on Significant Prominent Binary Patterns of Local Maximum Edge (SPBPLME)

By Rajendra Babu .Ch Sreenivasa Reddy. E Prabhakara Rao. B

DOI: https://doi.org/10.5815/ijitcs.2015.06.04, Pub. Date: 8 May 2015

This paper derives a new procedure for age classification of facial image based on the local region of facial image. The local region of facial image is extracted from a Significant Binary Pattern of Local Maximum Edge (SBPLME). The SBPLME is generated by calculating the absolute value of local difference between the average of local 3×3 sub window pixel values and its neighbors instead of the center pixel value. In the case of Local Maximum Edge Binary Pattern (LMEBP) calculating the absolute value of local difference between the center pixel value of local 3×3 sub window and its neighbors. The proposed SBPLME can generate 512 (0 to 511) different patterns. The present paper utilized Prominent LBP (PLBP) on the proposed SBPLME. The PLBP contains the significant patterns of Uniform LBP (ULBP) and Non Uniform LBP (NULBP). Thus the derived Significant PLBP of Local Maximum Edge (SPBPLME) becomes an efficient image classification and analysis, which will have a significant role in many areas. The novelty of the proposed SPBPLME method is, it has shown excellent age classification results by reducing the overall dimension, thus reducing the overall complexity.

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