What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
Artificial intelligence, Pattern recognition, Machine learning, Facial expression and Mathematical optimization are his primary areas of study.
His Artificial intelligence research focuses on subjects like Computer vision, which are linked to Activity recognition.
His Pattern recognition study combines topics in areas such as Contextual image classification and Feature.
The study incorporates disciplines such as Facial recognition system and Outlier in addition to Machine learning.
His Facial expression research is multidisciplinary, incorporating elements of Margin, Speech recognition and Neuroscience.
Fernando De la Torre combines subjects such as Algorithm, Stochastic gradient descent and Backpropagation with his study of Mathematical optimization.
His most cited work include:
- Supervised Descent Method and Its Applications to Face Alignment (1614 citations)
- A Framework for Robust Subspace Learning (546 citations)
- Facing Imbalanced Data--Recommendations for the Use of Performance Metrics (276 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Artificial intelligence, Pattern recognition, Computer vision, Machine learning and Facial expression.
Support vector machine, Discriminative model, Robustness, Facial recognition system and Feature extraction are among the areas of Artificial intelligence where the researcher is concentrating his efforts.
Fernando De la Torre interconnects Pose and Outlier in the investigation of issues within Discriminative model.
His Pattern recognition research focuses on Cluster analysis and how it relates to Synthetic data.
His research in Machine learning intersects with topics in Classifier and Kernel.
As a member of one scientific family, he mostly works in the field of Facial expression, focusing on Speech recognition and, on occasion, Facial Action Coding System.
He most often published in these fields:
- Artificial intelligence (80.75%)
- Pattern recognition (39.57%)
- Computer vision (28.88%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (80.75%)
- Computer vision (28.88%)
- Deep learning (4.81%)
In recent papers he was focusing on the following fields of study:
Fernando De la Torre focuses on Artificial intelligence, Computer vision, Deep learning, Robustness and Human–computer interaction.
Fernando De la Torre has researched Artificial intelligence in several fields, including Scalability and Pattern recognition.
His work carried out in the field of Pattern recognition brings together such families of science as Artificial neural network and Multilayer perceptron.
His Monocular, Pose and Rendering study, which is part of a larger body of work in Computer vision, is frequently linked to Unit and Generative model, bridging the gap between disciplines.
His studies deal with areas such as Cross-validation and Word error rate as well as Robustness.
In his research, Functional data analysis, Linear regression and Least squares is intimately related to Face, which falls under the overarching field of Human–computer interaction.
Between 2017 and 2021, his most popular works were:
- A Functional Regression Approach to Facial Landmark Tracking (25 citations)
- Increasing Robustness in the Detection of Freezing of Gait in Parkinson’s Disease (20 citations)
- Inverse Composition Discriminative Optimization for Point Cloud Registration (15 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
His main research concerns Artificial intelligence, Pattern recognition, Convolutional neural network, Deep learning and Codec.
Many of his studies on Artificial intelligence apply to Computer vision as well.
His work on Segmentation and Image segmentation as part of general Pattern recognition study is frequently linked to Weighting, bridging the gap between disciplines.
His research integrates issues of Network architecture, Visualization and Hybrid system in his study of Convolutional neural network.
His Deep learning research includes themes of Speech processing, Word error rate, Artificial neural network, Multilayer perceptron and Mel-frequency cepstrum.
As part of the same scientific family, Fernando De la Torre usually focuses on Motion, concentrating on Face and intersecting with Functional data analysis and Least squares.
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