Sreenivasa Reddy. E

Work place: ANU College of Engineering & Technology , Guntur, 522510, India



Research Interests: Computer systems and computational processes, Pattern Recognition, Computer Architecture and Organization, Image Manipulation, Image Processing


Sreenivas Reddy E: Received the B.Tech degree in Electronics & Communication Engineering from Nagarjuna University, India in 1988, M.S. degree from Birla Institute of Technology and Science, India in 1997, M.Tech degree in Computer Science from Visveswaraiah Technological University, India in 2000 and Ph.D in computer science from Acharya Nagarjuna University, India in 2008. He is the senior member of IEEE and presented 11 papers in international conferences and 6 journal papers. Presently working as principal in ANU college of Engineering. His research interest includes image processing, biometrics and pattern recognition.

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:, 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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