Dasari Haritha

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

E-mail: harithadasari9@yahoo.com


Research Interests: Computer Architecture and Organization, Image Compression, Computer Networks, Network Architecture, Image Processing, Data Structures and Algorithms


D. Haritha is Assistant Professor in Computer science and Engineering Department at Jawaharlal Nehru Technological University Kakinada. She has 12+ years of experience. She guided 30 M.Tech students and 15 MCA students for their project. Her research interest is on image processing, Data structures and networking. She published 5 research papers in international journals. She published 3 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: https://doi.org/10.5815/ijmecs.2012.11.02, 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.

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