Towards Generic Modelling of Viewer Interest Using Facial Expression and Heart Rate Features | Zendy

Prithwi Raj Chakraborty | Zendy; Dian Wirawan Tjondronegoro | Zendy; Ligang Zhang | Zendy; Vinod Chandran | Zendy

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Towards Generic Modelling of Viewer Interest Using Facial Expression and Heart Rate Features

Author(s) -

Prithwi Raj Chakraborty,

Dian Wirawan Tjondronegoro,

Ligang Zhang,

Vinod Chandran

Publication year - 2018

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2018.2874892

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

Automatic detection of viewer interest while watching video contents can enable multimedia applications, such as online video streaming, to recommend contents in real time. However, there is yet a generic model for detecting viewer interest that is independent of subject and content while using noninvasive sensors in near-natural settings. This paper is the first attempt at solving this issue by investigating the feasibility of a generic model for detecting viewer interest based on facial expression and heart rate features. The proposed model adopts deep learning features, which are trained and tested using multisubjects' data across different video stimuli domains. The experimental results show that the generic model can reach a similar accuracy to a domain-specific model.

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