D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 59 Citations 12,538 506 World Ranking 1712 National Ranking 48

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Computer vision
  • Statistics

His primary scientific interests are in Artificial intelligence, Pattern recognition, Speech recognition, Computer vision and Feature extraction. His study in Machine learning extends to Artificial intelligence with its themes. His study on Pattern recognition is mostly dedicated to connecting different topics, such as Identification.

In his study, Language identification is strongly linked to Mixture model, which falls under the umbrella field of Speech recognition. His Feature extraction research integrates issues from Gaze, Facial expression, Active appearance model and Human visual system model. Sridha Sridharan focuses mostly in the field of Cepstrum, narrowing it down to topics relating to Feature and, in certain cases, Noise.

His most cited work include:

  • Feature Warping for Robust Speaker Verification (561 citations)
  • Crowd Counting Using Multiple Local Features (221 citations)
  • i-vector based speaker recognition on short utterances (192 citations)

What are the main themes of his work throughout his whole career to date?

Sridha Sridharan focuses on Artificial intelligence, Speech recognition, Pattern recognition, Computer vision and Feature extraction. His work is dedicated to discovering how Artificial intelligence, Machine learning are connected with Context and other disciplines. His study in Speaker recognition, Speaker diarisation, Speech processing, NIST and Speaker verification is done as part of Speech recognition.

The study incorporates disciplines such as Feature and Feature in addition to Pattern recognition. His study in Video tracking, Tracking, Pixel, Biometrics and Object detection falls under the purview of Computer vision. Three-dimensional face recognition is the focus of his Facial recognition system research.

He most often published in these fields:

  • Artificial intelligence (72.31%)
  • Speech recognition (34.74%)
  • Pattern recognition (34.62%)

What were the highlights of his more recent work (between 2016-2021)?

  • Artificial intelligence (72.31%)
  • Machine learning (13.85%)
  • Pattern recognition (34.62%)

In recent papers he was focusing on the following fields of study:

Sridha Sridharan mostly deals with Artificial intelligence, Machine learning, Pattern recognition, Deep learning and Feature extraction. Sridha Sridharan interconnects Context and Computer vision in the investigation of issues within Artificial intelligence. The Machine learning study combines topics in areas such as State and Robustness.

His Pattern recognition research includes elements of Feature and Feature. His Feature extraction study integrates concerns from other disciplines, such as Facial recognition system, Facial expression, Task analysis and Hidden Markov model. The Artificial neural network study which covers Speech recognition that intersects with Mel-frequency cepstrum.

Between 2016 and 2021, his most popular works were:

  • Iris Recognition With Off-the-Shelf CNN Features: A Deep Learning Perspective (145 citations)
  • Soft + Hardwired attention: An LSTM framework for human trajectory prediction and abnormal event detection. (128 citations)
  • Long range iris recognition (77 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Statistics
  • Machine learning

His primary areas of study are Artificial intelligence, Deep learning, Machine learning, Feature extraction and Pattern recognition. His Artificial intelligence research includes themes of Context and Computer vision. His work in the fields of Machine learning, such as Support vector machine, intersects with other areas such as Component.

His study in Feature extraction is interdisciplinary in nature, drawing from both Emotion recognition, Facial expression, Task analysis and Synthetic data. His Facial expression study combines topics from a wide range of disciplines, such as Speech recognition and Face. His biological study spans a wide range of topics, including Noise measurement and Image translation.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Feature Warping for Robust Speaker Verification

Jason W. Pelecanos;Sridha Sridharan.
Proceedings of 2001 A Speaker Odyssey: The Speaker Recognition Workshop (2001)

918 Citations

Crowd Counting Using Multiple Local Features

David Ryan;Simon Denman;Clinton Fookes;Sridha Sridharan.
digital image computing: techniques and applications (2009)

280 Citations

Automatically Detecting Pain in Video Through Facial Action Units

P Lucey;J F Cohn;I Matthews;S Lucey.
systems man and cybernetics (2011)

267 Citations

Texture for script identification

A. Busch;W.W. Boles;S. Sridharan.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2005)

240 Citations

i-vector based speaker recognition on short utterances

Ahilan Kanagasundaram;Robbie Vogt;David Dean;Sridha Sridharan.
conference of the international speech communication association (2011)

192 Citations

Explicit modelling of session variability for speaker verification

Robbie Vogt;Sridha Sridharan.
Computer Speech & Language (2008)

177 Citations

Iris Recognition With Off-the-Shelf CNN Features: A Deep Learning Perspective

Kien Nguyen;Clinton Fookes;Arun Ross;Sridha Sridharan.
IEEE Access (2018)

177 Citations

Real-time adaptive background segmentation

D. Butler;S. Sridharan;V.M.Jr. Bove.
international conference on acoustics, speech, and signal processing (2003)

162 Citations

A Database for Person Re-Identification in Multi-Camera Surveillance Networks

Alina Bialkowski;Simon Denman;Sridha Sridharan;Clinton Fookes.
digital image computing techniques and applications (2012)

155 Citations

Face authentication test on the BANCA database

K. Messer;J. Kittler;M. Sadeghi;M. Hamouz.
international conference on pattern recognition (2004)

148 Citations

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