H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 51 Citations 8,030 283 World Ranking 2787 National Ranking 36

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Computer vision

His primary areas of investigation include Artificial intelligence, Gesture recognition, Computer vision, Machine learning and Gesture. His Artificial intelligence study frequently draws parallels with other fields, such as Pattern recognition. His research in Pattern recognition intersects with topics in Embedding, Categorization and Statistical model.

His research investigates the connection between Gesture recognition and topics such as Field that intersect with issues in Human–computer interaction. His work in the fields of Machine learning, such as Support vector machine, intersects with other areas such as Tree structure. His Gesture research includes themes of Dynamic time warping, Speech recognition, Facial expression and Lexicon.

His most cited work include:

  • On the Decoding Process in Ternary Error-Correcting Output Codes (198 citations)
  • Traffic Sign Recognition Using Evolutionary Adaboost Detection and Forest-ECOC Classification (165 citations)
  • Chalearn looking at people challenge 2014: Dataset and results (154 citations)

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

Sergio Escalera focuses on Artificial intelligence, Pattern recognition, Computer vision, Machine learning and Segmentation. Deep learning, RGB color model, Facial recognition system, Classifier and Gesture recognition are subfields of Artificial intelligence in which his conducts study. His Pattern recognition research includes elements of Random forest and Invariant.

His study involves Pixel, Pose and Object, a branch of Computer vision. The Machine learning study combines topics in areas such as Field and Categorization. Sergio Escalera has included themes like Speech recognition and Dynamic time warping in his Gesture study.

He most often published in these fields:

  • Artificial intelligence (79.35%)
  • Pattern recognition (34.78%)
  • Computer vision (25.54%)

What were the highlights of his more recent work (between 2017-2021)?

  • Artificial intelligence (79.35%)
  • Pattern recognition (34.78%)
  • Deep learning (12.23%)

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

Sergio Escalera mainly focuses on Artificial intelligence, Pattern recognition, Deep learning, Facial recognition system and Computer vision. His work deals with themes such as Context and Machine learning, which intersect with Artificial intelligence. His Pattern recognition research is multidisciplinary, relying on both RGB color model and Feature.

His Deep learning research integrates issues from Contextual image classification, Sign language and Feature extraction. In his work, Disgust is strongly intertwined with Facial expression, which is a subfield of Facial recognition system. In general Computer vision, his work in Noise reduction and Inpainting is often linked to Sequence linking many areas of study.

Between 2017 and 2021, his most popular works were:

  • RGB-D-based human motion recognition with deep learning: A survey (151 citations)
  • Depth-Based 3D Hand Pose Estimation: From Current Achievements to Future Goals (129 citations)
  • Audio-Visual Emotion Recognition in Video Clips (72 citations)

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

  • Artificial intelligence
  • Machine learning
  • Computer vision

Artificial intelligence, Pattern recognition, Deep learning, Convolutional neural network and Feature extraction are his primary areas of study. His Artificial intelligence study frequently links to adjacent areas such as Computer vision. His Pattern recognition research is multidisciplinary, incorporating elements of Graphical model and Gesture.

His biological study spans a wide range of topics, including Activity recognition, Recurrent neural network, Pose and Focus. His Convolutional neural network study combines topics in areas such as Frame, Embedding, Information loss, Boosting and One-hot. The various areas that Sergio Escalera examines in his Feature extraction study include Visualization, Emotion classification and Feature vector.

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

On the Decoding Process in Ternary Error-Correcting Output Codes

S. Escalera;O. Pujol;P. Radeva.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2010)

272 Citations

Traffic Sign Recognition Using Evolutionary Adaboost Detection and Forest-ECOC Classification

X. Baro;S. Escalera;J. Vitria;O. Pujol.
IEEE Transactions on Intelligent Transportation Systems (2009)

253 Citations

Chalearn looking at people challenge 2014: Dataset and results

Sergio Escalera;Xavier Baró;Jordi Gonzàlez;Miguel Ángel Bautista.
european conference on computer vision (2014)

221 Citations

Featureweighting in dynamic timewarping for gesture recognition in depth data

Miguel Reyes;Gabriel Dominguez;Sergio Escalera.
international conference on computer vision (2011)

215 Citations

Multi-modal gesture recognition challenge 2013: dataset and results

Sergio Escalera;Jordi Gonzàlez;Xavier Baró;Miguel Reyes.
international conference on multimodal interfaces (2013)

201 Citations

RGB-D-based human motion recognition with deep learning: A survey

Pichao Wang;Wanqing Li;Philip O Ogunbona;Jun Wan.
Computer Vision and Image Understanding (2018)

198 Citations

ChaLearn Looking at People RGB-D Isolated and Continuous Datasets for Gesture Recognition

Jun Wan;Stan Z. Li;Yibing Zhao;Shuai Zhou.
computer vision and pattern recognition (2016)

171 Citations

ChaLearn Looking at People 2015: Apparent Age and Cultural Event Recognition Datasets and Results

Sergio Escalera;Junior Fabian;Pablo Pardo;Xavier Baro.
international conference on computer vision (2015)

161 Citations

Depth-Based 3D Hand Pose Estimation: From Current Achievements to Future Goals

Shanxin Yuan;Guillermo Garcia-Hernando;Bjorn Stenger;Gyeongsik Moon.
computer vision and pattern recognition (2018)

151 Citations

A Survey on Deep Learning Based Approaches for Action and Gesture Recognition in Image Sequences

Maryam Asadi-Aghbolaghi;Albert Clapes;Marco Bellantonio;Hugo Jair Escalante.
ieee international conference on automatic face gesture recognition (2017)

141 Citations

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