Gourav Siddhad

Gourav Siddhad
Indian Institute of Technology Roorkee | University of Roorkee · Computer Science & Engineering

Doctor of Philosophy
Research Scholar at Indian Institute of Technology Roorkee

About

7
Publications
533
Reads
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6
Citations
Citations since 2016
7 Research Items
6 Citations
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Introduction
I am a research scholar (Ph.D.) in Dept. of CSE, IITR, under the supervision of Dr. Partha Pratim Roy (Associate Professor, CSE, IITR). My research interests lie in the field of Brain-Computer Interface, Cancelable Biometrics, Computer Vision, and Machine Learning.
Education
August 2020 - July 2024
Indian Institute of Technology Roorkee
Field of study
  • Computer Science
July 2013 - May 2017
University of Delhi
Field of study
  • Computer Science

Publications

Publications (7)
Chapter
Convolutional autoencoders are a great tool for extracting features from images and compressing them to a lower dimension called latent space. A latent space vector is generated from the input images by extracting the relevant and the most useful features required for approximating the images. In the proposed work, a convolutional autoencoder is us...
Thesis
The use of biometrics for access control on hand-held devices such as smartphones and tablets is in common use now. The security of biometrics and privacy of users’ data has become more important in this scenario. The templates stored in the database or a network are prone to different attacks. Since, biometrics are unique to an individual, their l...
Article
Full-text available
Cognitive workload is a crucial factor in tasks involving dynamic decision-making and other real-time and high-risk situations. Neuroimaging techniques have long been used for estimating cognitive workload. Given the portability, cost-effectiveness and high time-resolution of EEG as compared to fMRI and other neuroimaging modalities, an efficient m...
Preprint
Full-text available
With the unprecedented success of transformer networks in natural language processing (NLP), recently, they have been successfully adapted to areas like computer vision, generative adversarial networks (GAN), and reinforcement learning. Classifying electroencephalogram (EEG) data has been challenging and researchers have been overly dependent on pr...
Chapter
The security of biometric systems has always been a challenging area of research to safeguard against the day-by-day introduction of new attacks with the advancement in technology. Cancelable biometric templates have proved to be an effective measure against these attacks while ensuring an individual’s privacy. The proposed scheme uses a convolutio...
Article
Deep learning-based generative networks have brought a significant change in the generation of synthetic biometric data. Synthetic biometric data finds applications in developing biometric systems and testing them on a large amount of data to analyze their performance on extreme load scenarios or run simulation for health care personnel training. G...
Article
Biometrics are widely used in security systems, but it has drawbacks such as its nonreusability, being susceptible to getting stolen, and being prone to unauthorized access. These issues can be tackled using cancelable biometrics. In this work, features extracted using Log-Gabor filters are processed according to the proposed max-min thresholding....

Network

Projects

Projects (2)
Project
Develop efficient methods for the classification of EEG data.
Project
Develop Cancelable Biometric Template generation methods for low-end devices.