Multimodal Deep Learning Methods For Person Annotation In Video Sequences
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Multimodal Deep Learning Methods for Person Annotation in Video Sequences
In unsupervised identity recognition in video sequences systems, which is a very active field of research in computer vision, the use of convolutional neural networks (CNN's) is currently gaining a lot of interest due to the great results that this techniques have been shown in face recognition and verification problems in recent years. In this thesis, the improvement of a CNN applied for face verification will be made in the context of an unsupervised identity annotation system developed for the MediaEval 2016 task. This improvement will be achieved by training the 2016 CNN architecture with images from the task database, which is now possible since we can use the last version outputs, along with a data augmentation method applied to the previously extracted samples. In addition, a new multimodal verification system is implemented merging both visual and audio feature vectors. An evaluation of the margin of improvement that these techniques introduce in the whole system will be made, comparing against the State-of-the-Art. Finally some conclusions will be exposed based on the obtained results will be drawn along with some possible future lines of work.
Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction
This book constitutes the refereed post-workshop proceedings of the 5th IAPR TC9 Workshop on Pattern Recognition of Social Signals in Human-Computer-Interaction, MPRSS 2018, held in Beijing, China, in August 2018. The 10 revised papers presented in this book focus on pattern recognition, machine learning and information fusion methods with applications in social signal processing, including multimodal emotion recognition and pain intensity estimation, especially the question how to distinguish between human emotions from pain or stress induced by pain is discussed.
Machine Learning for Multimodal Interaction
This book constitutes the thoroughly refereed post-proceedings of the First International Workshop on Machine Learning for Multimodal Interaction, MLMI 2004, held in Martigny, Switzerland in June 2004. The 30 revised full papers presented were carefully selected during two rounds of reviewing and revision. The papers are organized in topical sections on HCI and applications, structuring and interaction, multimodal processing, speech processing, dialogue management, and vision and emotion.