What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
The scientist’s investigation covers issues in Artificial intelligence, Facial expression, Speech recognition, Machine learning and Emotion recognition.
His studies in Artificial intelligence integrate themes in fields like Computer vision and Pattern recognition.
His Speech recognition research includes themes of Sentence and Classifier.
Roland Goecke combines subjects such as Closed captioning, Field, Sequence learning and Benchmark with his study of Machine learning.
The Emotion recognition study combines topics in areas such as Emotion classification and Affect.
The concepts of his Facial recognition system study are interwoven with issues in Image retrieval and Gesture recognition.
His most cited work include:
- Collecting Large, Richly Annotated Facial-Expression Databases from Movies (360 citations)
- Static facial expression analysis in tough conditions: Data, evaluation protocol and benchmark (236 citations)
- Video and Image based Emotion Recognition Challenges in the Wild: EmotiW 2015 (202 citations)
What are the main themes of his work throughout his whole career to date?
Roland Goecke spends much of his time researching Artificial intelligence, Computer vision, Pattern recognition, Speech recognition and Facial expression.
The study of Artificial intelligence is intertwined with the study of Machine learning in a number of ways.
His biological study spans a wide range of topics, including Bag-of-words model, Gesture recognition and Benchmark.
His work deals with themes such as Histogram, Eye tracking and Robustness, which intersect with Pattern recognition.
His studies deal with areas such as Modality and Australian English as well as Speech recognition.
His Facial expression research incorporates themes from Emotion recognition and Happiness.
He most often published in these fields:
- Artificial intelligence (77.60%)
- Computer vision (33.33%)
- Pattern recognition (32.24%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (77.60%)
- Affective computing (15.85%)
- Affect (8.20%)
In recent papers he was focusing on the following fields of study:
Roland Goecke mostly deals with Artificial intelligence, Affective computing, Affect, Convolutional neural network and Computer vision.
His Artificial intelligence research is multidisciplinary, relying on both Machine learning and Pattern recognition.
The study incorporates disciplines such as Group emotion and Mood in addition to Affective computing.
He has researched Convolutional neural network in several fields, including Discriminative model and Face.
His Computer vision research includes elements of Gait, Gait analysis and Force platform.
His work in Facial expression tackles topics such as Speech recognition which are related to areas like Affective stimuli.
Between 2016 and 2021, his most popular works were:
- From individual to group-level emotion recognition: EmotiW 5.0 (129 citations)
- EmotiW 2018: Audio-Video, Student Engagement and Group-Level Affect Prediction (53 citations)
- Multimodal Depression Detection: Fusion Analysis of Paralinguistic, Head Pose and Eye Gaze Behaviors (48 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Affect, Human–computer interaction, Artificial intelligence, Affective computing and Adversarial system are his primary areas of study.
His Affect study incorporates themes from Machine learning, Fusion and Social group.
His research in Artificial intelligence focuses on subjects like Computer vision, which are connected to Facial movement and Physical medicine and rehabilitation.
His Affective computing research is multidisciplinary, incorporating perspectives in Emotion recognition and Multimodal interaction.
His research integrates issues of Facial expression and Benchmark in his study of Emotion recognition.
In his study, which falls under the umbrella issue of Speech recognition, Facial recognition system is strongly linked to Feature extraction.
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