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

Dr. J. Aravinth joined the Department of Electronics and Communication Engineering, Amrita Vishwa Vidyapeetham in the year 2010, where he is currently an Assistant Professor (Senior Grade). He received his B.E. degree in Electronics and Communication from Periyar University, Salem, India in 2004. He received his M.E degree in Applied Electronics from Anna University, Chennai, India in 2007. He earned his Ph.D. degree from Anna University, Chennai, in December 2017. His Ph.D. thesis was Design of Single and Hybrid Classifier based Score Level Fusion for Multimodal Biometric Recognition. Before joining Amrita, he worked as a lecturer in the Department of ECE at Kongu Engineering College, Erode during the period 2007-2010.

His research areas of interest include Multimodal Biometrics, Hyperspectral Remote Sensing, LiDAR data processing and Soft Computing. He is currently working on Hyperspectral remote sensing for urban development. He has authored around 15 technical papers in reputed conferences and journals indexed in Scopus. He is a member of the Institution of Electronics and Telecommunication Engineers, a professional body. He has organized several national workshops in association with CoreEl Technologies, Bangalore and ISRO sponsored national seminar.

Research Expertise

  • Design and development of fusion algorithms for Multimodal Biometrics
  • Developing a spectral based classification for urban object detection using Multispectral and HyperSpectral data
  • Identifying pollution content in an urban area using Hyperspectral Remote Sensing
  • Mineral Exploration using Hyperspectral remote sensing
  • LiDAR data processing for 3D City modelling
  • Identification of leaf diseases in different crops using machine learning algorithms

Teaching

  • Signals & Systems
  • Digital Signal Processing
  • Digital Image Processing
  • Solid State Devices
  • Electronics Engineering
  • Electronic Circuits
  • Microwave Engineering

UG / PG Student Projects

  1. Feature Extraction for Multimodal Biometric and Fusion using Gaussian Mixture Model
  2. Multimodal score fusion using cuckoo search algorithm
  3. Multimodal Biometric recognition based on serial fusion
  4. Robust features for spoofing detection
  5. Detection of Copper in southern India using hyperion imagery
  6. Detection of pollution content in urban objects from hyperspectral thermal data
  7. Detection of pollution content in an urban area using landsat 8 image
  8. Verification of next generation ICS300 satellite phone software requirements
  9. Pixel level fusion of hyperspectral image and LiDAR data
  10. Analysis of peaks and canopy heights of the building using LiDAR data
  11. Detection and classification of urban objects from HSR-HTIR data

Workshops Organized

  • Two days International Workshop on “Creative Thinking and User Centred Design” during May 15th & 16th 2018 at Amrita School of Engineering, Coimbatore
  • Two days International Workshop on International Workshop on “Rural Healthcare in India” during May 18th & 19th 2018 at Amrita School of Engineering, Coimbatore
  • Two days ISRO sponsored National Workshop on “Role of University in Empowering Indian Villages” during September 21 - 23, 2016 at Amrita School of Engineering, Coimbatore
  • Two days ISRO sponsored National seminar on “Techniques and Applications of Hyperspectral Image analysis” during 19 - 20, April 2016 at Amrita School of Engineering, Coimbatore
  • Two days national level workshop on “Xilinx Vivado System Generator and Analog Discovery Kit”, Department of ECE, Amrita School of Engineering, Coimbatore in association with CoreEl Technologies, Bangalore during 21-22, September 2015
  • Two days National level workshop on “Embedded Design Flow using Xilinx ZynQ SoC” Department of ECE, Amrita School of Engineering, Coimbatore in association with CoreEl Technologies, Bangalore during 27-28 February 2015
  • Two days National level workshop on “Signal and Image Processing Applications using Xilinx System Generator” Department of ECE, Amrita School of Engineering, Coimbatore in association with coreEL Technologies, Bangalore during 10-11 April 2014.
  • Two week ISTE workshop on "Signals & Systems" during January 2 to 12, 2014 at Amrita School of Engineering, Coimbatore.
  • Four days IETE sponsored National level workshop on “Advances in Signal and Image Processing” during May 29 to 2 June, 2012 at Amrita School of Engineering, Coimbatore.

Publications

Publication Type: Conference Proceedings

Year of Publication Publication Type Title

2017

Conference Proceedings

R. Anand, S. Veni, and Aravinth J., “Big Data Challenges in Airborne Hyperspectral Image for Urban Landuse Classification”, 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI). 2017.[Abstract]


In recent years, it was a difficult task to classify a huge set of data due to the increasing population in urban places. As of now, satellite hyperspectral image provides information but this is not sufficient to classify data in urban areas. To develop the urban areas, accurate and timely information is necessary for the government. Hence, airborne hyperspectral data provides sufficient information for urban planning and disaster management. This paper, focuses on the following objectives: (i) To improve the classification accuracy in bigdata images (ii) To reduce the mixed pixels in residential buildings that are surrounded by small trees (iii) To bring down similar pixels of roads and parking lots. In this paper, 15 different classes were classified which are important for the growth in urban areas. The SVM classifier has more accuracy and better kappa coefficient compared with Neural Network (NN) and K-Means clustering. The Overall Accuracy (OA) has improved by 23.3.

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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 Publication Publication Type Title

2017

Conference Paper

J. E. George, Aravinth J., and S. Veni, “Detection of Pollution Content in an Urban Area Using Landsat 8 Data”, in 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2017.[Abstract]


Pollution control is a challenging task in current scenario. The very first step to control pollution is to detect the sources of pollution. The urban areas are more polluted than rural due to the high population density. The pollutants considered in this paper are aerosol and asbestos sheets. The source of asbestos are building roofs which are mainly in urban area and that of aerosol is combustion of coal. The conventional image processing techniques failed to detect the pollutant in urban environment which can be performed well using multispectral imaging. Since each object has different temperatures using the TIR (Thermal Infrared) bands of Landsat 8 data, the urban objects are classified using the land surface temperature map. The presence of asbestos sheets is detected by change in intensity of images with respect to Band 7 (Short Wave Infrared) and Band 9 (Cirrus). Aerosol is comprised of components that cause air pollution. In this work, the PM10 value is considered as one of the measures to identify the concentration of particulate matters in specific area.

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2017

Conference Paper

S. Roopa, Narayanan, K. S., Sarvanan, S., Jhanane, M., and Aravinth J., “Detection of Copper in Southern India Using Hyperion Imagery”, in 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2017.[Abstract]


The demand for mineral and energy resources has increased pressure to reduce the environmental and social impact through mining operations. Among various minerals, Copper has served as an effective barometer of the economic health of a particular region or locality. Copper is an essential nutrient for humans and a few mammals for the best functioning of enzymes and carbohydrate metabolism. Copper is also needed for the formation of hemoglobin and hemocyanin which helps in oxygen transportation pigments in the blood of shellfish and many other vertebrates. Copper has high health benefits for a healthy existence, as the mineral allows a normal metabolic process associated with vitamins and amino acids. All these cannot be produced within the body and hence it needs to be added from external sources. This paper indicates the identification of Copper in parts of Karnataka using Hyperspectral remote sensing techniques. ENVI software is used to process and analyze the sensor's (Hyperion) raw data to determine the exact locations of copper that are present in it using different classification techniques.

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2016

Conference Paper

S. Prasad and Aravinth J., “Serial multimodal framework for enhancing user convenience using dimensionality reduction technique”, in Proceedings of IEEE International Conference on Circuit, Power and Computing Technologies, ICCPCT 2016, 2016.[Abstract]


Owing to an increased demand for identity management system in day to day applications, considering user convenience as a priority, we conduct a survey on biometric systems and observed serial multimodal systems as the most user convenient and reliable system. For further enhancement of the system performance, the discriminating capacity of the weaker trait can be improved, and we expect that the use of a semi supervised learning based dimensionality reduction method will make the performance of the weaker trait matcher better and hence enhance the overall performance of the system. We also propose a serial biometric system of face and fingerprint into which the dimensionality reduction method can be incorporated.

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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 »»

2014

Conference Paper

H. Nadella, .Sampurna, R., .Susnehalatha, N., Viswanathan, V., T., V. Kumar, and Aravinth J., “Multimodal Biometric Score Fusion Using Cuckoo Search Algorithm”, in Proceedings of RTCSP-2014 , Amrita Vishwa Vidyapeetham, 2014.

2012

Conference Paper

A. Vivek S, S, S., P, N., M, V., Ravi, A., and Aravinth J., “Density Based Score Fusion of Fingerprint, Iris and Face for User Authentication”, in Proceedings of fifth National Conference on Recent trends in Information and Communication Technology(RTICT-2012), Bannari Amman Institute of Technology , Sathyamangalam, 2012.

2010

Conference Paper

D. Zhang and Aravinth J., “Palmprint Authentication System”, in Third National Conference on Electrical and Instrumnetation Systems, 2010.

2010

Conference Paper

Aravinth J., thangam, M. A., .Aboorva, B., and Banupriya, M., “Database Construction for Facial Expressions using CBIR”, in Third National Conference on Electrical and Instrumnetation Systems , 2010.

Publication Type: Journal Article

Year of Publication 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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2012

Journal Article

Aravinth J. and S.Valarmathy, D., “A Novel Feature Extraction Techniques for Multimodal Score Fusion Using Density Based Gaussian Mixture Model Approach”, International Journal of Emerging Technology and Advanced Engineering, vol. 2, pp. 123-131, 2012.[Abstract]


An Unimodal biometric systems, which relies only on a single trait of a person for identification is often not able to meet the desired performance. Combining multiple biometrics may enhance the performance of personal authentication system in accuracy and reliability which is adopted in multimodal biometrics. This paper describes the feature extraction techniques for three modalities viz. fingerprint, iris and face. The extracted information is stored as a template which can be fused using density based score level fusion (using GMM followed by likelihood ratio test).

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Faculty Research Interest: 
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