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Abhishek Sharma
Researcher at Indian Institute of Technology Guwahati
Publications - 16
Citations - 263
Abhishek Sharma is an academic researcher from Indian Institute of Technology Guwahati. The author has contributed to research in topics: Dynamic time warping & Vector quantization. The author has an hindex of 5, co-authored 16 publications receiving 177 citations. Previous affiliations of Abhishek Sharma include Himachal Pradesh University & Indian Institutes of Information Technology.
Papers
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Journal ArticleDOI
An enhanced contextual DTW based system for online signature verification using Vector Quantization
Abhishek Sharma,Suresh Sundaram +1 more
TL;DR: This work presents an enhanced Dynamic Time Warping (DTW) based online signature verification system by utilizing the code-vectors generated from a Vector-Quantization (VQ) strategy, which is the first of its kind, that exploits the characteristics of the warping path for online signatures verification.
Journal ArticleDOI
On the Exploration of Information From the DTW Cost Matrix for Online Signature Verification
Abhishek Sharma,Suresh Sundaram +1 more
TL;DR: This paper explores the utility of information derived from the dynamic time warping (DTW) cost matrix for the problem of online signature verification and devise a score that utilizes the information from the cost matrix and warping path alignments for authenticating the veracity of a test signature.
Journal ArticleDOI
A Novel Online Signature Verification System Based on GMM Features in a DTW Framework
Abhishek Sharma,Suresh Sundaram +1 more
TL;DR: This paper proposes the use of a set of features derived from a Gaussian mixture model (GMM) for the alignment of the signatures using DTW, the first of its kind that uses the features of the GMM, a model-based classifier into the framework of the DTW technique for online signature verification.
Journal ArticleDOI
DeepFuseOSV: online signature verification using hybrid feature fusion and depthwise separable convolution neural network architecture
TL;DR: A competent feature fusion technique in which traditional statistical-based features are fused with deep representations from a convolutional auto-encoder and a hybrid architecture combining depth-wise separable convolution neural network (DWSCNN) and long short term memory (LSTM) network delivering state-of-the-art performance for OSV is proposed.
Proceedings ArticleDOI
Photoplethysmogram Based Mean Arterial Pressure Estimation Using LSTM
TL;DR: In this paper, the authors proposed a direct strategy for the estimation of mean arterial pressure (MAP) without using the systolic and diastolic BP values, by exploring 13 significant morphological features from a single PPG signal which are most related to the target MAP.