Assessing General Well-Being using De-identified Features

Authors

  • Insu Song
  • John Vong

Keywords:

Facial, palsy, Anonymous feature, SVM, face, SOM, Health Informatics, eHealth, Medical Data Analysis

Abstract

The UN has predicted that cell-phone ownership will reach 5 billion in 2010. This proliferation of cell phones and connectivity offers an unprecedented opportunity to access vast populations, including previously hard-to-reach populations in rural areas and mountainous zones and underserved populations. Cell phones now can provide capabilities for the developing world that includes text, image processing and image displays. The available standardized interfaces can be leveraged to create powerful systems. In particular, digital cameras of cell phones provide easy to use interfaces for capturing useful information on the general wellbeing and emotive features of individuals. However, photographic images contain private and sensitive personal information in its raw form and thus considered unsuitable for online services. Therefore, there is a need for a computational algorithm for extracting anonymous digital features (for example, Hamming distance) from captured facial expression images for estimating different states of well-being. We have developed computer algorithms predicting well-being states from anonymous facial expression features. The research outcome can be used in a variety of online services including suggesting useful health information to improve general wellbeing.

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Published

2014-07-01

How to Cite

Insu Song, & John Vong. (2014). Assessing General Well-Being using De-identified Features. Journal of Network and Innovative Computing, 2, 9. Retrieved from https://cspub-jnic.org/index.php/jnic/article/view/68

Issue

Vol. 2 (2014)

Section

Original Article

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