Statistical Machine Learning for Human Behaviour Analysis

This Special Issue focused on novel vision-based approaches, mainly related to computer vision and machine learning, for the automatic analysis of human behaviour. We solicited submissions on the following topics: information theory-based pattern classification, biometric recognition, multimodal hum...

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Other Authors: Moeslund, Thomas (Editor), Escalera, Sergio (Editor), Anbarjafari, Gholamreza (Editor), Nasrollahi, Kamal (Editor), Wan, Jun (Editor)
Format: Electronic Book Chapter
Language:English
Published: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2020
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DOAB: description of the publication
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520 |a This Special Issue focused on novel vision-based approaches, mainly related to computer vision and machine learning, for the automatic analysis of human behaviour. We solicited submissions on the following topics: information theory-based pattern classification, biometric recognition, multimodal human analysis, low resolution human activity analysis, face analysis, abnormal behaviour analysis, unsupervised human analysis scenarios, 3D/4D human pose and shape estimation, human analysis in virtual/augmented reality, affective computing, social signal processing, personality computing, activity recognition, human tracking in the wild, and application of information-theoretic concepts for human behaviour analysis. In the end, 15 papers were accepted for this special issue. These papers, that are reviewed in this editorial, analyse human behaviour from the aforementioned perspectives, defining in most of the cases the state of the art in their corresponding field. 
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653 |a categorical data 
653 |a hand sign language 
653 |a deep learning 
653 |a restricted Boltzmann machine (RBM) 
653 |a multi-modal 
653 |a profoundly deaf 
653 |a noisy image 
653 |a ensemble methods 
653 |a adaptive classifiers 
653 |a recurrent concepts 
653 |a concept drift 
653 |a stock price direction prediction 
653 |a toe-off detection 
653 |a gait event 
653 |a silhouettes difference 
653 |a convolutional neural network 
653 |a saliency detection 
653 |a foggy image 
653 |a spatial domain 
653 |a frequency domain 
653 |a object contour detection 
653 |a discrete stationary wavelet transform 
653 |a attention allocation 
653 |a attention behavior 
653 |a hybrid entropy 
653 |a information entropy 
653 |a single pixel single photon image acquisition 
653 |a time-of-flight 
653 |a action recognition 
653 |a fibromyalgia 
653 |a Learning Using Concave and Convex Kernels 
653 |a Empatica E4 
653 |a self-reported survey 
653 |a speech emotion recognition 
653 |a 3D convolutional neural networks 
653 |a k-means clustering 
653 |a spectrograms 
653 |a context-aware framework 
653 |a accuracy 
653 |a false negative rate 
653 |a individual behavior estimation 
653 |a statistical-based time-frequency domain and crowd condition 
653 |a emotion recognition 
653 |a gestures 
653 |a body movements 
653 |a Kinect sensor 
653 |a neural networks 
653 |a face analysis 
653 |a face segmentation 
653 |a head pose estimation 
653 |a age classification 
653 |a gender classification 
653 |a singular point detection 
653 |a boundary segmentation 
653 |a blurring detection 
653 |a fingerprint image enhancement 
653 |a fingerprint quality 
653 |a speech 
653 |a committee of classifiers 
653 |a biometric recognition 
653 |a multimodal-based human identification 
653 |a privacy 
653 |a privacy-aware 
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