Buch, Englisch, Band 10165, 163 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 2759 g
Video Analytics. Face and Facial Expression Recognition and Audience Measurement
1. Auflage 2017
ISBN: 978-3-319-56686-3
Verlag: Springer International Publishing
Third International Workshop, VAAM 2016, and Second International Workshop, FFER 2016, Cancun, Mexico, December 4, 2016, Revised Selected Papers
Buch, Englisch, Band 10165, 163 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 2759 g
Reihe: Lecture Notes in Computer Science
ISBN: 978-3-319-56686-3
Verlag: Springer International Publishing
The 11 papers presented in this volume were carefully reviewed and selected from 13 submissions. They deal with: re-identification; consumer behavior analysis; utilizing pupillary response for task difficulty measurement; logo detection; saliency prediction; classification of facial expressions; face recognition; face verification; age estimation; super resolution; pose estimation; and pain recognition.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
Person Re-Identification Dataset with RGB-D Camera in a Top-View Configuration.- Pervasive System for Consumer Behaviour Analysis in Retail Environments.- Estimation of task difficulty and habituation effect while visual manipulation using pupillary response.- Robust Probabilistic Logo Detection in Broadcast Videos for Audience Measurement.- Saliency Prediction for Visual Regions of Interest with Applications in Advertising.- Person Invariant Classification of Subtle Facial Expressions using Coded Movement Direction of Keypoints.- A Two-directional Two-Dimensional PCA Correlation Filter in the Phase Only Spectrum for Face Recognition in Video.- End to End Deep Learning for Single Step Real-time Facial Expression Recognition.- Comparative Study of Human Age Estimation Based on Hand-crafted and Deep Face Features.- Pose-Selective Max Pooling for Measuring Similarity.- Complementing SRCNN by Transformed Self-Exemplars. -Human Head Pose Estimation on SASE database using Random Hough Regression Forests.- Spatio-Temporal Pain Recognition in CNN-based Super-Resolved Facial Images.