LEADER 04838nam 2201213z- 450 001 9910557288403321 005 20231214133134.0 035 $a(CKB)5400000000041156 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/68631 035 $a(EXLCZ)995400000000041156 100 $a20202105d2020 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aStatistical Machine Learning for Human Behaviour Analysis 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2020 215 $a1 electronic resource (300 p.) 311 $a3-03936-228-3 311 $a3-03936-229-1 330 $aThis 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. 606 $aHistory of engineering & technology$2bicssc 610 $amulti-objective evolutionary algorithms 610 $arule-based classifiers 610 $ainterpretable machine learning 610 $acategorical data 610 $ahand sign language 610 $adeep learning 610 $arestricted Boltzmann machine (RBM) 610 $amulti-modal 610 $aprofoundly deaf 610 $anoisy image 610 $aensemble methods 610 $aadaptive classifiers 610 $arecurrent concepts 610 $aconcept drift 610 $astock price direction prediction 610 $atoe-off detection 610 $agait event 610 $asilhouettes difference 610 $aconvolutional neural network 610 $asaliency detection 610 $afoggy image 610 $aspatial domain 610 $afrequency domain 610 $aobject contour detection 610 $adiscrete stationary wavelet transform 610 $aattention allocation 610 $aattention behavior 610 $ahybrid entropy 610 $ainformation entropy 610 $asingle pixel single photon image acquisition 610 $atime-of-flight 610 $aaction recognition 610 $afibromyalgia 610 $aLearning Using Concave and Convex Kernels 610 $aEmpatica E4 610 $aself-reported survey 610 $aspeech emotion recognition 610 $a3D convolutional neural networks 610 $ak-means clustering 610 $aspectrograms 610 $acontext-aware framework 610 $aaccuracy 610 $afalse negative rate 610 $aindividual behavior estimation 610 $astatistical-based time-frequency domain and crowd condition 610 $aemotion recognition 610 $agestures 610 $abody movements 610 $aKinect sensor 610 $aneural networks 610 $aface analysis 610 $aface segmentation 610 $ahead pose estimation 610 $aage classification 610 $agender classification 610 $asingular point detection 610 $aboundary segmentation 610 $ablurring detection 610 $afingerprint image enhancement 610 $afingerprint quality 610 $aspeech 610 $acommittee of classifiers 610 $abiometric recognition 610 $amultimodal-based human identification 610 $aprivacy 610 $aprivacy-aware 615 7$aHistory of engineering & technology 700 $aMoeslund$b Thomas$4edt$01324788 702 $aEscalera$b Sergio$4edt 702 $aAnbarjafari$b Gholamreza$4edt 702 $aNasrollahi$b Kamal$4edt 702 $aWan$b Jun$4edt 702 $aMoeslund$b Thomas$4oth 702 $aEscalera$b Sergio$4oth 702 $aAnbarjafari$b Gholamreza$4oth 702 $aNasrollahi$b Kamal$4oth 702 $aWan$b Jun$4oth 906 $aBOOK 912 $a9910557288403321 996 $aStatistical Machine Learning for Human Behaviour Analysis$93036300 997 $aUNINA