Dr. T. Senthil Kumar currently serves as an Associate Professor at the Department of Computer Science and Engineering at Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore. His research interest include Video Analytics, Big Data Analytics, Intrusion Detection Systems .

He completed his B. Tech. (Computer Science and Engineering) from Sethu Institute of Technology, Madurai. He then completed his M. Tech. (Distributed Computing Systems) from Pondicherry Engineering college, Pondicherry. He completed his PhD in Information and Communication Engineering from Anna University, Chennai.

He has received appreciation in Indian Express for agent based programming for Banking Domain. He has published in nine National Conferences, 12 International Conference, 14 International Journal and ten Edited Book volume. He has 22 scopus publications. He has published a Book on C++. He is a reviewer for Elsevier Computers & Electrical Engineering Journal.

He is  guiding scholar with Amrita Vishwa Vidyapeetham in the area of Video Analytics, Intrusion detection system. He teaches courses in Video Processing, Video Analytics, Machine Learning for Big Data. He has involved himself in developing in the competency areas of programming like Matlab, NS2, JIST, DotNet, Android, Hadoop,Spark, OpenCV with Qt He is working with Amrita Vishwa Vidyapeetham, Coimbatore since 2001. He has membership in professional bodies like CSI,IETE.

Funding projects

Malware detection using FPGA, Sandboxing and Machine Learning 


Publication Type: Journal Article
Year of Publication Publication Type Title
2016 Journal Article R. G., Kumar, TaSenthil, Reyner, P. P. D., Leela, G., Mangayarkarasi, N., Abirami, A., and Vinayaka, K., “3D modelling of a jewellery and its virtual try-on”, Advances in Intelligent Systems and Computing, vol. 397, pp. 157-165, 2016.[Abstract]

Nowadays, everything is becoming automated. So, automation is indeed needed in the world of jewellery. The goldsmith or any jewellery vendor, rather than having all the real patterns of jewellery, can have the model of these jewellery, so that he can display them virtually on the customer’s hand using Augmented Reality. 2D representation of an object deals only with the height and the width of an object. 3D representations include the third dimension of an image which is the depth information of an object. This paper presents an overall approach to 3D modelling of jewellery from the uncalibrated images. The datasets are taken from different viewing planes at different intervals. From these images, we construct the 3D model of an object. 3D model provides a realistic view for the users by projecting it on human hand using the augmented reality technique. © Springer India 2016.

More »»
2016 Journal Article K. S. Gautam and Kumar, TaSenthil, “Discrimination and detection of face and non-face using multilayer feedforward perceptron”, Advances in Intelligent Systems and Computing, vol. 397, pp. 89-103, 2016.[Abstract]

<p>The paper proposes a face detection system that locates and extracts faces from the background using the multilayer feedforward perceptron. Facial features are extracted from the local image using filters. In this approach, feature vector from Gabor filter acts as an input for the multilayer feedforward perceptron. The points holding high information on face image are used for extraction of feature vectors. Since Gabor filter extracts features from varying scales and orientations, the feature points are extracted with high accuracy. Experimental results show the multilayer feedforward perceptron discriminates and detects faces from non-face patterns irrespective of the illumination changes. © Springer India 2016.</p>

More »»
2016 Journal Article A. Sankar, Bharathi, P. D., Midhun, M., Vijay, K., and Kumar, TaSenthil, “A conjectural study on machine learning algorithms”, Advances in Intelligent Systems and Computing, vol. 397, pp. 105-116, 2016.[Abstract]

<p>Artificial Intelligence, a field which deals with the study and design of systems, which has the capability of observing its environment and does functionalities which aims at maximizing the probability of its success in solving problems. AI turned out to be a field which captured wide interest and attention from the scientific world, so that it gained extraordinary growth. This in turn resulted in the increased focus on a field—which deals with developing the underlying conjectures of learning aspects and learning machines—machine learning. The methodologies and objectives of machine learning played a vital role in the considerable progress gained by AI. Machine learning aims at improving the learning capabilities of intelligent systems. This survey is aimed at providing a theoretical insight into the major algorithms that are used in machine learning and the basic methodology followed in them. © Springer India 2016.</p>

More »»
2015 Journal Article TaSenthil Kumar and Pandey, S., “Customization of recommendation system using collaborative filtering algorithm on cloud using mahout”, Advances in Intelligent Systems and Computing, vol. 321, 2015.[Abstract]

Recommendation System helps people in decision making regarding an item/person. Growth of World Wide Web and E-commerce are the catalyst for recommendation system. Due to large size of data, recommendation system suffers from scalability problem. Hadoop is one of the solutions for this problem. Collaborative filtering is a machine learning algorithm and Mahout is an open source java library which favors collaborative filtering on Hadoop environment. The paper discusses on how recommendation system using collaborative filtering is possible using Mahout environment. The performance of the approach has been presented using Speedup and efficiency. More »»
2015 Journal Article TaSenthil Kumar and Saivenkateswaran, Sb, “Evaluation of video analytics for face detection and recognition”, International Journal of Applied Engineering Research, vol. 10, pp. 24003-24016, 2015.[Abstract]

<p>Face Detection and Recognition is presenting a challenging approach in the field of computer vision and Image processing [www.cosy.sbg.ac.at]. To localize and to extract the particular face region in the image or video, Face Detection is used as the first step for Face Recognition systems [www.idsia.ch] [1]. Face detection and recognition has several applications. They are content based image or video retrieval, video coding, video conferencing, crowd analysis, intelligent human computer interfaces [iasir.net][1]. Still many researches are going on because it is very tough to find the exact face of a person if we need to match the face region in the database that makes face detection a tough problem in computer vision [iasir.net].This paper analyzes how Face Detection and Recognition approaches can be used for a wide variety of applications like smart buildings, driver recognition during accidents. © Research India Publications.</p>

More »»
2014 Journal Article TaSenthil Kumar, Suresh, A., and Karumathil, A., “Improvised classification model for cloud based authentication using keystroke dynamics”, Lecture Notes in Electrical Engineering, vol. 309 LNEE, pp. 295-303, 2014.[Abstract]

<p>The etymology of communication is the transmission of data. Data has to be transmitted through different devices, network topologies and geographic locations. The strength of communication has tripled with the advent of cloud technologies providing high scalability and storage on demand. The need for cloud security is increasing in an alarming rate and using biometric techniques over traditional password based alternative has proved to be efficient. A behavioral biometric such as keystroke dynamics can be used to strengthen existing security techniques effectively.Due to the semi-autonomous nature of the typing behavior of an individual it is difficult to validate the identity of the user. This paper proposes a model to validate the identity of the user which acclimatizes to tolerance across multiple devices and provides a robust three dimensional model for classification. As an additional layer of security the model is transformed after every login to prevent professional intruders from predicting the acceptance region. © 2014 Springer-Verlag Berlin Heidelberg.</p>

More »»
2014 Journal Article TaSenthil Kumar, Reddy, P. K. Ajay, Chidambaram, M., Anurag, D. V., Karthik, S., K. Teja, R., and N. Harish, S., “Video Recommender In Open/Closed Systems”, International Journal of Research in Engineering and Technology, vol. 3, pp. 24 - 28, 2014.
2014 Journal Article TaSenthil Kumar, Suresh, A., Pai, K. Kiron, and Chinnaswamy, P., “Survey on Predictive medical data analysis”, Journal of Engineering Research & Technology, vol. 3, pp. 2283-2286, 2014.
2014 Journal Article TaSenthil Kumar, Vishak, J., Sanjeev, S., and Sneha, B., “Cloud Based Framework for Road Accident Analysis”, International Journal of Computer Science and Mobile Computing, vol. 3, pp. 1025 - 1032, 2014.
2014 Journal Article TaSenthil Kumar and Vijai, A., “3D Reconstruction of Face: A comparison of Marching Cube and Improved Marching Cube Algorithm”, International Journal of Advances in Image Processing Techniques, vol. 1, pp. 6-9, 2014.
2013 Journal Article TaSenthil Kumar, .Sivanandam, N., Gokul, M., and Anusha, B., “Logo Classification of Vehicles using SURF based on Low Detailed Feature Recognition”, International Journal of Computer Applications, vol. 3, pp. 5 - 7, 2013.
2013 Journal Article TaSenthil Kumar, Kumar, S., and , “A Novel Face Recognition Algorithm using PCA”, International Journal of Computer Applications, vol. 3, pp. 8 - 12, 2013.
2013 Journal Article S. Murugesan, Kumar, TaSenthil, Priyanka, U. Sree, and Abinaya, K., “Towards an Approach for Improved Security in Wireless Networks”, International Journal of Computer Applications, vol. 1, pp. 9-13, 2013.
2013 Journal Article R. Manoj, Kumar, TaSenthil, Maruthi, M., and Vivek, G., “A Survey: Artificial Neural Networks in Surveillance System”, International Journal of Computer Applications, vol. 1, pp. 19-22, 2013.
2013 Journal Article N. Susan Thampi, Kumar, TaSenthil, and Johnpaul, C. I., “Performance Analysis of Various Recommendation Algorithms Using Apache Hadoop and Mahout”, International Journal of Scientific & Engineering Research, vol. 4, pp. 279-287, 2013.
2012 Journal Article TaSenthil Kumar and Sivanandam, S. Nb, “An improved approach for detecting car in video using neural network model”, Journal of Computer Science, vol. 8, pp. 1759-1768, 2012.[Abstract]

The study represents a novel approach taken towards car detection, feature extraction and classification in a video. Though many methods have been proposed to deal with individual features of a vehicle, like edge, license plate, corners, no system has been implemented to combine features. Combination of four unique features, namely, color, shape, number plate and logo gives the application a stronghold on various applications like surveillance recording to detect accident percentage(for every make of a company), authentication of a car in the Parliament(for high security), learning system(readily available knowledge for automobile tyro enthusiasts) with increased accuracy of matching. Video surveillance is a security solution for government buildings, facilities and operations. Installing this system can enhance existing security systems or help start a comprehensive security solution that can keep the building, employees and records safe. The system uses a Haar cascaded classifier to detect a car in a video and implements an efficient algorithm to extract the color of it along with the confidence rating. An gadabouts trained classifier is used to detect the logo (Suzuki/Toyota/Hyunadai) of the car whose accuracy is enhanced by implementing SURF matching. A combination of blobs and contour tracing is applied for shape detection and model classification while number plate detection is performed in a smart and efficient algorithm which uses morphological operations and contour tracing. Finally, a trained, single perceptron neural network model is integrated with the system for identifying the make of the car. A thorough work on the system has proved it to be efficient and accurate, under different illumination conditions, when tested with a huge dataset which has been collected over a period of six months. © 2012 Science Publications.

More »»
2012 Journal Article TaSenthil Kumar, Sivanandam, S. Nb, and Akhila, G. Pc, “Detection of car in video using soft computing techniques”, Communications in Computer and Information Science, vol. 270 CCIS, pp. 556-565, 2012.[Abstract]

The features indicate the characteristics of the object. The features vary from object to object like colour, size, shape, texture etc. Natural images can be decomposed into constituent objects, which are in turn composed of features. The corners or edges of the object can be considered as part of feature extraction. The edges / corner detection is also complex for certain objects as it has varied characteristics due to other objects in representation. The other examples of features include motion in image sequences, curves, boundaries between different image regions, properties of region. Feature extraction is the process of transforming of high-dimensional data into a meaningful representation of reduced dimensionality. The identified features are beneficial to mitigate the computational complexity and improve the accuracy of a particular classifier. This paper suggests mechanism for selection of appropriate technique for detecting object like car in video. © 2012 Springer-Verlag.

More »»
2012 Journal Article TaSenthil Kumar and Sivanandam, S. Nb, “A modified approach for detecting car in video using feature extraction techniques”, European Journal of Scientific Research, vol. 77, pp. 134-144, 2012.[Abstract]

<p>Deployment of effective surveillance and security measures is important in these days. The proposed approach is able to detect, identify and track of different types of vehicles and people entering the secured premises, to avoid any mishap from happening. There are many existing approaches which are used for tracking objects. Edge matching, Divide-and-Conquer search, Gradient matching, Histograms of receptive field responses, Pose clustering, SIFT, SURF etc are some of the approaches applied. All these methods are either Appearance based methods or Feature based methods. They lag in one or the other way when it comes to real time applications.So there has been a need for creating a new system that could combine positive aspects of both the methods and increase the efficiency in tracking objects, when it comes to real life scenario. A novel approach for car detection and classification is presented, to a whole new level, by devising a system that takes the video of a vehicle as input, detects and classifies the vehicle based on its make and model. It takes into consideration four prominent features namely Logo of vehicle, its number plate, colour and shape. Logo detector and recognizer algorithms are implemented to find the manufacturer of the vehicle. The detection process is based on the Adaboost algorithm, which is a cascade of binary features to rapidly locate and detect logos. The number plate region is localized and extracted using blob extraction method. Then colour of the vehicle is retrieved by applying Haar cascade classifier to first localize on the vehicle region and then applying a novel algorithm to find colour. Shape of the vehicle is also extracted using blob extraction method. The classification is done by a very efficient algorithm called Support vector machines. Experimental results show that our system is a viable approach and achieves good feature extraction and classification rates across a range of videos with vehicles under different conditions. © EuroJournals Publishing, Inc. 2012.</p>

More »»
2012 Journal Article S. N. Sivanandam, Kumar, TaSenthil, kumar, krishna, and Ajay, A., “An Improved Approach for Character recognition in vehicle Number plate using Eigenfeature Regularisation and Extraction Method”, International Journal of Research and Reviews in Electrical and Computer Engineering, vol. 2, pp. 64-69, 2012.
Publication Type: Book Chapter
Year of Publication Publication Type Title
2016 Book Chapter S. V. Girish, Prakash, R., Swetha, S. N. H., Pareek, G., Kumar, TaSenthil, and A. Ganesh, B., “Video Analysis for Malpractice Detection in Classroom Examination”, P. L. Suresh and Panigrahi, K. Bijaya New Delhi: Springer India, 2016, pp. 209–217.[Abstract]

This paper describes a wireless sensor network-based indoor air quality monitoring system. The indoor air quality defines the quality of the environment where people live. Here wireless sensor network serves as the tool for estimating the indoor air quality. The WSN comprises sensor nodes and a coordinator node which communicates using the IEEE 802.15.4 wireless standard ZigBee wireless module. The indoor air quality estimation is done by interfacing CO2, temperature and RH (Relative humidity) sensors with the sensor node. The sensor node gathers the sensor data and reports it to the coordinator for real-time monitoring using a GUI (Graphical user interface) developed in Java NetBeans to run on windows PC. The collected data can be used to maintain the environment parameters by interfacing it to a HVAC (Heating, Ventilation and Air Conditioning) system.

More »»
2016 Book Chapter V. Reghu and Kumar, TaSenthil, “Gesture Controlled Automation for Physically Impaired”, P. L. Suresh and Panigrahi, K. Bijaya New Delhi: Springer India, 2016, pp. 673–683.[Abstract]

Hand gesture recognition system is widely used for interfacing between computer and human using hand gesture. This work presents a room for automation system using hand gestures, which is meant for physically impaired. The objective of project is to develop an algorithm for recognition of hand gestures with reasonable accuracy. Most of the gesture recognition system fails when the background is complex, here in our method we use hand detection in complex background. Hu moments are used as feature which is used to classify the gestures. Kinect sensor is used to get the input video which will give the depth map from which we will get the location of the people performing gesture. Two gestures are used to switching on and off function for an Electrical appliance. Arduino Board is used to interface between the computer and the appliances.

More »»
2016 Book Chapter S. V. Girish, Prakash, R., Swetha, S. N. H., Pareek, G., and Kumar, TaSenthil, “Advances in Intelligent Systems and Computing”, in A Network Model of GUI-Based Implementation of Sensor Node for Indoor Air Quality Monitoring, vol. 397, New Delhi: Springer India, 2016, pp. 209–217.
2015 Book Chapter TaSenthil Kumar and Prakash, K. I. Ohhm, “A Queueing Model for e-Learning System”, P. L. Suresh, Dash, S. Subhransu, and Panigrahi, K. Bijaya New Delhi: Springer India, 2015, pp. 89–94.[Abstract]

There has been much written about e-Learning practice; however, little attention has been given to come out with a mathematical model for e-Learning. As the lack of a proper mathematical model will hinder providing better service to the customers, we have come up with an attempt to make a study on which of the existing mathematical models could fit e-Learning. We argue with statistical data that (M/M/C): (∞/FIFO) is one of the models which best fit e-Learning. This paper aims to provide inputs that the suggested queuing model can be used for e-Learning system in real conditions.

More »»
Publication Type: Conference Paper
Year of Publication Publication Type Title
2016 Conference Paper N. M. Dhanya, Kumar, TaSenthil, Sujithra, C., Prasanth, S., and Shruthi, U. K., “Pedagogue: A Model for Improving Core Competency Level in Placement Interviews Through Interactive Android Application”, in Proceedings of the International Conference on Soft Computing Systems, 2016.[Abstract]

This paper discusses about developing a mobile application running on the cloud server. The Cloud acclaims a new era of computing, where application services are provided through the Internet. Though mobile systems are resource-constrained devices with limited computation power, memory, storage, and energy, the use of cloud computing enhances the capability of mobile systems by offering virtually unlimited dynamic resources for computation and storage. The challenge faced here is that traditional smartphones do not support cloud, these applications require specialized mobile cloud application model. The core innovativeness of the application lies in its delivery structure as an interactive android application centered on emerging technologies like mobile cloud computing–that improves the core competencies of the students by taking up online tests posted by the faculty in the campus. The performance of this application has been presented using scalability, accessibility, portability, security, data consistency, user session migration, and redirection delay

More »»
2016 Conference Paper S. Sreelakshmi, Vijai, A., and Kumar, TaSenthil, “Detection and Segmentation of Cluttered Objects from Texture Cluttered Scene”, in Proceedings of the International Conference on Soft Computing Systems, 2016.[Abstract]

The aim of this paper is to segment an object from a texture-cluttered image. Segmentation is achieved by extracting the local information of image and embedding it with active contour model based on region. Images with inhomogenous intensity can be segmented using this model by extracting the local information of image. The level set function [1] can be smoothened by introducing the Gaussian filtering to the current model and the need for resetting the contour for every iteration can be eliminated. Evaluation results showed that the results obtained from the proposed method is similar to the results obtained from LBF [2] (local binary fitting) energy model, but the proposed method is found to be more efficient in terms of computational aspect. Moreover, the method maintains the sub-pixel reliability and boundary fixing properties. The approach is presented with metrics of visual similarity and could be further extended with quantitative metrics.

More »»
2013 Conference Paper TaSenthil Kumar, Gajendran, V., Harshad, R., Aswani, S., and Narayanan, D. Sankara, “MEDISCRIPT-MOBILE CLOUD COLLABRATIVE SPEECH RECOGNITION FRAMEWORK”, in IJCA Proceedings on International Conference on Innovation in Communication, Information and Computing 2013, 2013.
Faculty Details


Faculty Email: