Qualification: 
M.E
Email: 
j_aravinth@cb.amrita.edu
Phone: 
9566772677, 9043672677

J. Aravinth received his B.E. (Electronics and Communication Engineering) degree from Periyar University, Salem in May 2004 and M.E. (Applied Electronics) degree from Anna University, Chennai in May 2007. He is pursuing his Ph.D. degree at Anna University, Chennai in the area of Biometrics. He is presently working as Assistant Professor (Senior Grade) in the Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore.

His research interest includes Multimodal Biometrics, Hperspectral Image Analysis, Biomedical Signal Processing and Soft Computing. He is the life member in Institution of Electronics and Telecommunication Engineers. He has published nearly 10 papers in International Journals and conferences.

Research Expertise

  • Multimodal Biometric Fusion (Pursuing PhD)
  • Hyperspectral Image Analysis and processing( PG Project)
     

Teaching

  • Signals & Systems
  • Digital Signal processing
  • Solid State Devices
  • Electronics Engineering
  • Electronic Circuits I

Publications

Publication Type: Journal Article

Year of Conference Publication Type Title

2016

Journal Article

Aravinth J. and Valarmathy, S., “Multi classifier-based score level fusion of multi-modal biometric recognition and its application to remote biometrics authentication”, The Imaging Science Journal, vol. 64, pp. 1-14, 2016.[Abstract]


Biometric recognition has become a common and reliable way to authenticate the identity of a person. Multimodal biometrics has become an interest of areas for researches in the recent past as it provides more reliability and accuracy. In multimodal biometric recognition, score level fusion has been a very promising approach to improve the overall system's accuracy. In this paper, score level fusion is carried out using three categories of classifiers like, rule classifier (fuzzy classifier), lazy classifier (Naïve Bayes) and learning classifiers (ABC-NN). These three classifiers have their own advantages and disadvantages so the hybridization of classifiers leads to provide overall improvements. The proposed technique consists of three modules, namely processing module, classifier module and combination module. Finally, the proposed fusion method is applied to remote biometric authentication. The implementation is carried out using MATLAB and the evaluation metrics employed are False Acceptance Rate (FAR), False Rejection Rate (FRR) and accuracy. The proposed technique is also compared with other techniques and by employing various combinations of modalities. From the results, we can observe that the proposed technique has achieved better accuracy value and Receiver Operating Characteristic (ROC) curves when compared to other techniques. The proposed technique reached maximum accuracy of having 95% and shows the effectiveness of the proposed technique.

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2015

Journal Article

Aravinth J. and Valarmathy, Sb, “A Natural Optimization Algorithm to Fuse Scores for Multimodal Biometric Recognition”, International Journal of Applied Engineering Research, vol. 10, pp. 21341-21354, 2015.[Abstract]


Multimodal biometrics has become an interest of areas for researches in the recent past as it provides more reliability and accuracy. In this work, we have performed multimodal biometric score fusion with the help of neural networks. The two traits that have been selected for fusion are fingerprint and iris due to their effectiveness and good resistance to spoofing. The type of fusion employed in the system is score level fusion. The neural network classifier approach is chosen to take advantage of its good learning efficiency. The system trains the neural network using a recently developed evolutionary algorithm, the Cuckoo Search Algorithm. The experimental results shown that the proposed fusion system can provide us low FAR, FRR and maximum accuracy of 98.78%. © Research India Publications.

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2013

Journal Article

Aravinth J. and Valarmathy, Sb, “Score-level fusion technique for multi-modal biometric recognition using ABC-based neural network”, International Review on Computers and Software, vol. 8, pp. 1889-1900, 2013.[Abstract]


Biometric recognition has become a common and reliable way to authenticate the identity of a person. Multimodal biometric system utilizes two or more individual modalities so as to improve the recognition accuracy. The key to multimodal biometrics is the fusion of the various biometric data after feature extraction. In this paper, score level fusion technique for multi-modal biometric recognition using Artificial Bee Colony (ABC) based Neural Network (NN) is proposed. The technique consists of two phases namely feature extraction phase and score fusion phase. Features are extracted from the fingerprint, face and iris modalities in the feature extraction phase. Fusion of score value is carried out after obtaining the individual matching scores from the three modalities. Fusion of scores is based on neural network where, ABC algorithm is used as a training algorithm and based on the scores obtained from ABC-based neural network, the recognition is done. The implementation is done using MATLAB and the performance of the proposed technique is evaluated using FRR, FAR, accuracy and ROC curve. The proposed technique is compared with KNN technique and from the results we can see that our proposed technique has achieved better results by having lower FRR and FAR values and higher accuracy measure. © 2013 Praise Worthy Prize S.r.l. - All rights reserved.

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Publication Type: Conference Proceedings

Year of Conference Publication Type Title

2016

Conference Proceedings

R. Anand, S. Veni, and Aravinth J., “An Application of image processing techniques for Detection of Diseases on Brinjal Leaves Using K-Means Clustering Method”, IEEE International Conference on Circuit, Power and Computing Technologies”, ICCPCT. 2016.

2016

Conference Proceedings

Anand R, S. Veni, and Aravinth J., “An Application of image processing techniques for Detection of Diseases on Brinjal Leaves Using K-Means Clustering Method(2016)”, Fifth International Conference on Recent Trends in Information Technology 2016 (ICRTIT 2016). Anna University, Chennai campus , 2016.

2012

Conference Proceedings

S. A. Vivek, Aravinth J., and Valarmathy, S., “Feature extraction for multimodal biometric and study of fusion using Gaussian mixture model”, IEEE International Conference on Pattern Recognition, Informatics and Medical Engineering(PRIME 2012). Salem, Tamilnadu, pp. 387-392, 2012.[Abstract]


Biometrics consists of methods for uniquely recognizing humans based upon one or more intrinsic physical or behavioral traits. This paper describes the feature extraction techniques for three modalities viz. fingerprint, iris and face. The extracted information from each modality is stored as a template. The information are fused at the match score level using a density based score level fusion, GMM followed by the Likelihood ratio test. GMM parameters are estimated from training data using the iterative Expectation-Maximization (EM) algorithm. © 2012 IEEE.

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2012

Conference Proceedings

Aravinth J. and .S.Valarmathy, D., “Fusion of fingerprint, Face, and Iris for personal identification based on Expectation Maximization”, International Convention on Innovations in Engineering and Technology for Sustainable Development . Bannari Amman Institute of Technology, pp. 373-378, 2012.

Publication Type: Conference Paper

Year of Conference Publication Type Title

2016

Conference Paper

A. Sathya, Swetha, J., Das, K. A., George, K. K., Dr. Santhosh Kumar C., and Aravinth J., “Robust features for spoofing detection”, in 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2016.[Abstract]


It is very important to enhance the robustness of Automatic Speaker Verification (ASV) systems against spoofing attacks. One of the recent research efforts in this direction is to derive features that are robust against spoofed speech. In this work, we experiment with the use of Cosine Normalised Phase-based Cepstral Coefficients (CNPCC) as inputs to a Gaussian Mixture Model (GMM) back-end classifier and compare its results with systems developed using the popular short term cepstral features, Mel-Frequency Cepstral Coefficients (MFCC) and Power Normalised Cepstral Coefficients (PNCC), and show that CNPCC outperforms the other features. We then perform a score level fusion of the system developed using CNPCC with that of the systems using MFCC and PNCC to further enhance the performance. We use known attacks to train and optimise the system and unknown attacks to evaluate and present the results. More »»
Faculty Research Interest: 
207
PROGRAMS
OFFERED
5
AMRITA
CAMPUSES
15
CONSTITUENT
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GRADE BY
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9th
RANK(INDIA):
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150+
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