Qualification: 
M.Tech
r_karthika@cb.amrita.edu

R. Karthika currently serves as Assistant Professor (SR) at the department of Electronics and Communication Engineering, School of Engineering. he joined Amrita Vishwa Vidyapeetham in 2001. R. Karthika received the B. E. degree in Electronics and Communication from Madras University in 2000. She received her M.Tech degree in Computer Vision and Image Processing from Amrita Vishwa Vidyapeetham, Coimbatore. Currently she is pursuing Ph.D. in Amrita Vishwa Vidyapeetham. Her current research interests include pattern recognition, Image processing. She is a member in Institution of Electronics and Telecommunication Engineers (IETE).

Teaching

  • Signals and System
  • Digital Signal Processing
  • Pattern Recognition Techniques and Algorithms
  • Micro processors

Publications

Publication Type: Conference Proceedings

Year of Conference Title

2016

R. Karthika, Dr. Latha Parameswaran, B.K., P., and L.P., S., “Study of Gabor wavelet for face recognition invariant to pose and orientation”, Proceedings of the International Conference on Soft Computing Systems, Advances in Intelligent Systems and Computing, vol. 397. Springer Verlag, pp. 501-509, 2016.[Abstract]


Gabor filters have achieved enormous success in texture analysis, feature extraction, segmentation, iris and face recognition. Face recognition is one of the most popular biometric modalities which has wide range of applications in biometric authentication. The most useful property of a Gabor filter is that it can achieve multi-resolution and multi-orientation analysis of an image. This paper presents an algorithm using Gabor wavelets in capturing discriminatory content, obtained by convolving a face image with coefficients of Gabor filter with different orientations and scales. Support vector machine (SVM) has been used to construct a robust classifier. This method has been tested with publicly available ORL dataset. This algorithm has been tested, cross-validated and the detailed results are presented. It is inferred that the proposed method offers a recognition rate (94%) with tenfold cross-validation. © Springer India 2016.

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2014

A. .S, .M, J., Prakash, K., .R, S., and Karthika, R., “Detection Of Vehicles Using Image Descriptor”, Proceedings of SARC-ITR International Conference. pp. 44-48, 2014.

2014

S. M. Priya and Karthika, R., “Unsupervised Environmental Sound Recognition”, Proceedings of International Conference on Embedded Systems(ICES 2014). pp. 44-48, 2014.

2012

R. Karthika, ,, and PVS, M., “Global Portfolio Optimization for BSE Sensex using the Enhanced Black Litterman Model”, International Conference on Modeling Optimization Computing, Technology, vol. 38. 2012.

2012

R. Karthika, ,, Meghana, P. V. S., D, S., and , “Predicting BSE Sensex: Using Enhanced Black Litterman Model”, Fourth International Conference on Electronics Computer Technology, vol. 3. 2012.

2009

A. S, B, V., K, S., Krishnan, S., P, S., Karthika, R., and Sabarish Narayanan B., “Design of Planar Multi Layered Dielectric Structures using Binomial Matching Technique”, ELECTRON - Technical Report of Department of ECE, Special Issue, Proceedings of First National Conference on Recent Trends in Communication and Signal Processing (RTCSP’09), vol. 1. pp. 71-77, 2009.

2008

A. S, K, S., Krishnan, S., P, S., B, V., Sabarish Narayanan B., and Karthika, R., “Analysis and Performance of Planar Multi Layered Electromagnetic Absorbers for Shielding Applications”, National Symposium on Instrumentation (NSI 33). Vishakhapatnam, pp. 35-36, 2008.

Publication Type: Conference Paper

Year of Conference Title

2014

S. P. Mohanapriya, Sumesh, E. P., and Karthika, R., “Environmental sound recognition using Gaussian mixture model and neural network classifier”, in International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE), 2014.[Abstract]


Environmental sound recognition is an audio scene identification process in which a person's location is found by analyzing the background sound. This paper deals with the prototype modeling for environmental sound recognition. Sound recognition involves the collection of audio data, extraction of important features, clustering of similar features and their classification. The Mel frequency cepstrum co-efficients are extracted. These features are used for clustering by a Gaussian mixture model which is a probabilistic model. Neural Network classifier is used for classification of the features and to identify the environmental audio scene. The implementation is done with the help of MATLAB. Five major environmental sounds which include the sound of car, office, restaurant, street, subway are considered. This shows a better efficiency than the already existing method. The efficiency achieved in this method is 98.9%. More »»