Video Analytics. Face and Facial Expression Recognition and Audience Measurement, 1st ed. 2017 Third International Workshop, VAAM 2016, and Second International Workshop, FFER 2016, Cancun, Mexico, December 4, 2016, Revised Selected Papers Image Processing, Computer Vision, Pattern Recognition, and Graphics Series

This book constitutes the proceedings of the Third Workshop on Video Analytics for Audience Measurement, VAAM 2016, and the Second International Workshop on Face and Facial Expression Recognition from Real World Videos, FFER 2016, held at the 23rd International Conference on Pattern Recognition, ICPR 2016, in Cancun, Mexico, in December 2016. 

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. 

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.