Chittipotula Satyanarayana

Work place: University college of Engineering, Jawaharlal Nehru Technological University Kakinada, Kakinada



Research Interests: Computer systems and computational processes, Pattern Recognition, Network Architecture, Network Security, Image Processing, Speech Recognition, Speech Synthesis, Database Management System


Dr. Ch. Satyanarayana is Associate Professor in Computer science and Engineering Department at Jawaharlal Nehru Technological University Kakinada. He has 13 years of experience. His area of interest is on Image processing, Database Management Systems, Speech Recognition, Pattern recognition and network security. He guided more than 78 M.Tech projects and 56 MCA projects. He published 25 research papers in international journals. He published 35 research papers in international conferences.

Author Articles
Performance Evaluation on the Effect of Combining DCT and LBP on Face Recognition System

By Dasari Haritha Kraleti Srinivasa Rao Chittipotula Satyanarayana

DOI:, Pub. Date: 8 Nov. 2012

In this paper, we introduce a face recognition algorithm based on doubly truncated multivariate Gaussian mixture model with Discrete Cosine Transform (DCT) and Local binary pattern (LBP). Here, the input face image is transformed to the local binary pattern domain. The obtained local binary pattern image is divided into non-overlapping blocks. Then from each block the DCT coefficients are computed and feature vector is extracted. Assigning that the feature vector follows a doubly truncated multivariate Gaussian mixture distribution, the face image is modelled. By using the Expectation-Maximization algorithm the model parameters are estimated. The initialization of the model parameters is done by using either K-means algorithm or hierarchical clustering algorithm and moment method of estimation. The face recognition system is developed with the likelihood function under Bayesian frame. The efficiency of the developed face recognition system is evaluated by conducting experimentation with JNTUK and Yale face image databases. The performance measures like half total error rate, recognition rates are computed along with plotting the ROC curves. A comparative study of the developed algorithm with some of the earlier existing algorithm revealed that this system perform better since, it utilizes local and global information of the face.

[...] Read more.
Other Articles