Sivaiah Bellamkonda

Work place: Department of Computer Applications, National Institute of Technology, Tiruchirappalli - 620015, India



Research Interests: Image Processing, Neural Networks, Computational Learning Theory


Sivaiah is pursuing Ph.D. at Department of Computer Applications, National Institute of Technology, Tiruchirappalli. He obtained his B. Tech. and M. Tech in Computer Science & Engineering from Jawaharlal Nehru Technological University, Hyderabad, Andhra Pradesh, India in 2007 and 2010 respectively. His current research interests include image processing, neural networks, machine learning, and intelligent systems.

Author Articles
A Facial Expression Recognition Model using Support Vector Machines

By Sivaiah Bellamkonda N.P.Gopalan

DOI:, Pub. Date: 8 Nov. 2018

Facial Expression Recognition (FER) has gained interest among researchers due to its inevitable role in the human computer interaction. In this paper, an FER model is proposed using principal component analysis (PCA) as the dimensionality reduction technique, Gabor wavelets and Local binary pattern (LBP) as the feature extraction techniques and support vector machine (SVM) as the classification technique. The experimentation was done on Cohn-Kanade, JAFFE, MMI Facial Expression datasets and real time facial expressions using a webcam. The proposed methods outperform the existing methods surveyed.

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Pattern Averaging Technique for Facial Expression Recognition Using Support Vector Machines

By N.P.Gopalan Sivaiah Bellamkonda

DOI:, Pub. Date: 8 Sep. 2018

Facial expression is one of the nonverbal communication methods of identifying an emotional state of a human being.  Due to its crucial importance in Human-Robot interaction, facial expression recognition (FER) is in the limelight of recent research activities.  Most of the studies consider the whole expression images in their analysis, and it has several has several drawbacks concerning illumination, orientation, texture, zoom level, time and space complexity. In this paper, a novel feature extraction technique called the pattern averaging is studied on whole image data using reduction in the dimension of the image by averaging the neighboring pixels. The study is found to give better results on standard datasets using support vector machine classifier. 

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