Video Analytics. Face and Facial Expression Recognition and Audience Measurement

Third International Workshop, VAAM 2016, and Second International Workshop, FFER 2016, Cancun, Mexico, December 4, 2016, Revised Selected Papers

AvKamal Nasrollahi,Cosimo Distante

Häftad, Engelska, 2017

Del 10165 i serien Lecture Notes in Computer Science

550 kr

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2017-03-29
Mått:155 x 235 x 10 mm
Vikt:277 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:163
Förlag:Springer International Publishing AG
ISBN:9783319566863

Utforska kategorier

Systemvetenskap och AI inom Data och IT
Artificiell intelligens inom Data och IT
Hårdvara inom Data och IT

Innehållsförteckning

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.

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