Vinothkanna Rajendran
@vcenggw.ac.in
Professor & Electronics and Communication Engineering
Vivekanandha College of Engineering for Women
RESEARCH INTERESTS
Biometrics
Image Processing
Soft Computing
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
R. Vinothkanna, T. Vijayakumar, and N. Prabakaran
Inderscience Publishers
Kudamala Raveendra and R. Vinothkanna
Inderscience Publishers
Sampath Dakshina Murthy Achanta, Karthikeyan T., and Vinoth Kanna R.
Emerald
Purpose The recent advancement in gait analysis combines internet of things that provides better observations of person living behavior. The biomechanical model used for elderly and physically challenged persons is related to gait-related parameters, and the accuracy of the existing systems significantly varies according to different person abilities and their challenges. The paper aims to discuss these issues. Design/methodology/approach Deployment of wearable sensors in gait analysis provides a better solution while tracking the changes of the personal style, and this proposed model uses an electronics system using force sensing resistor and body sensors. Findings Experimental results provide an average gait recognition of 95 percent compared to the existing neural network-based gait analysis model based on the walking speeds and threshold values. Originality/value The sensors are used to monitor and update the predicted values of a person for analysis. Using IoT a communication process is performed in the research work by identifying a physically challenged person even in crowded areas.
R. Vinothkanna, N. Prabakaran, and S. Sivakannan
Inderscience Publishers
R. Vinothkanna, S. Sivakannan, and N. Prabakaran
Inderscience Publishers
R. Vinothkanna and Amitabh Wahi
Inderscience Publishers
R. Vinothkanna and P. K. Sasikumar
Springer International Publishing
We propose a novel multimodal biometric system that uses fingerprint and gait recognition traits. Fingerprint images are gathered with the help of a touchless optical sensor with total internal reflection and a capacitive line sensor. Gait samples are obtained as continuous images in 3 frames from a camera in burst mode. Features from these images are extracted with the help of Minutia Cylinder-Code (MCC) minutia descriptor. Further, the images are compared with the gathered sample database of 80 sample images. The comparison score is normalized using geometric mean. Fusion of the normalized images is done using contourlet derivative weighted rank fusion. A combination of these techniques provides an improved performance for authentication using biometrics.
Sampath Dakshina Murthy Achanta, T. Karthikeyan, and R. Vinothkanna
Springer Science and Business Media LLC
Internet of things plays vital role in real-time applications, and the research thrust towards implementing IoT in gait analysis increases day by day in order to obtain efficient gait recognition mechanism. IoT in gait analysis is used to monitor and communicate the observing gait, and also to transfer data to others is the current trend which is available. This research work provides an efficient gait recognition system with IoT using dynamic time wrapping and naïve bays classifier as combination to obtain hybrid model. The objective of this research is identifying the patients or persons with walking disabilities in a crowded area and providing suitable alerts to them by monitoring the walking styles. So that the possibility of getting injured is avoided and the information related to the persons also alerted through IoT module. Also, IoT module is used to collect information from the sensors used in persons accessories and other places. Twenty-five males and 10 females are subjected to examine the proposed model in different locations and achieved the overall accuracy percentage of 92.15%.
Raveendra K and R. Vinothkanna
Elsevier BV
Abstract An organization uses a symbol as its representation in the market for ease of identification and uniqueness. Logos are used to identify and retrieve the materials, even in a complex environment for further analysis. Algorithms based on support vector machine and neural networks provide better results in retrieval of the document from small dataset. But inlarge data sets the existing models lags in their classification performance. This proposed model uses ant colony optimization (ACO) along with the local descriptor scale-invariant feature transform (SIFT), as a hybrid model for retrieving document from dataset. This hybrid model enhances the performance of the retrieval model in terms of increased efficiency, leading to an accuracy of 95.86% with a high output precision of 97.67%.
R. Vinothkanna and T. Vijayakumar
Springer International Publishing
Face is a highly non-rigid object; in such case, face detection and recognition has become an essential part of biometric systems in the majority of the applications. Numerous applications like robots, tablets, surveillance systems, and cell phones revolve around an efficient face detection and recognition technique in the background for access. Human–computer interaction systems like expression recognition, cognitive state/emotional state, etc. are used. Recognizing with the increased need for security and anticipation of spoofing attacks, almost all techniques have been proposed in the past to successfully detect and recognize the face through a single or combination of facial features, which is a challenging task given the complex nature of the background and the number of facial features involved. Here, the proposed work involves a multi-resolution technique, namely, the Contourlet transform along with linear discriminant analysis for feature detection given to an RBFN classifier for effective classification. It could be clearly seen that the proposed technique outperforms the other conventional techniques by its recognition rate of nearly 99.2%. The observed results indicate a good classification rate in comparison with conventional techniques.
R. Vinothkanna and K. Santhi
Inderscience Publishers