Statistical Machine Learning for Human Behaviour Analysis

• Autres auteurs: Moeslund, Thomas (Éditeur intellectuel), Escalera, Sergio (Éditeur intellectuel), Anbarjafari, Gholamreza (Éditeur intellectuel), Nasrollahi, Kamal (Éditeur intellectuel), Wan, Jun (Éditeur intellectuel)
• Format: Électronique Chapitre de livre
• Langue: anglais
• Publié: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2020
• Sujets: History of engineering & technology; multi-objective evolutionary algorithms; rule-based classifiers; interpretable machine learning; categorical data; hand sign language; deep learning; restricted Boltzmann machine (RBM); multi-modal; profoundly deaf; noisy image; ensemble methods; adaptive classifiers; recurrent concepts; concept drift; stock price direction prediction; toe-off detection; gait event; silhouettes difference; convolutional neural network; saliency detection; foggy image; spatial domain; frequency domain; object contour detection; discrete stationary wavelet transform; attention allocation; attention behavior; hybrid entropy; information entropy; single pixel single photon image acquisition; time-of-flight; action recognition; fibromyalgia; Learning Using Concave and Convex Kernels; Empatica E4; self-reported survey; speech emotion recognition; 3D convolutional neural networks; k-means clustering; spectrograms; context-aware framework; accuracy; false negative rate; individual behavior estimation; statistical-based time-frequency domain and crowd condition; emotion recognition; gestures; body movements; Kinect sensor; neural networks; face analysis; face segmentation; head pose estimation; age classification; gender classification; singular point detection; boundary segmentation; blurring detection; fingerprint image enhancement; fingerprint quality; speech; committee of classifiers; biometric recognition; multimodal-based human identification; privacy; privacy-aware
• Accès en ligne: DOAB: download the publication; DOAB: description of the publication

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...