Gaussian hamming distance: de-identified features of facial expressions
Conference Publication ResearchOnline@JCUAbstract
We present new image features for diagnosing general wellbeing states and medical conditions. The new method, called Gaussian Hamming Distance (GHD), generates de-identified features that are highly correlated with general wellbeing states, such as happiness, smoking, and facial palsy. This new method will allow aid organizations and governments in developing countries to provide affordable medical services. The available standardized interfaces of cell-phones will allow us to create powerful medical diagnostics systems using photographic images without revealing private and sensitive personal information. We evaluate the new approach using real face-image data and four classifiers: Naive Bayesian classier, Artificial Neural Network, Decision Tree, and Support Vector Machines (SVM) for predicting general wellbeing states. Its predictive power (over 93% accuracy) is suitable for providing a variety of online services including recommending useful health information for improving general wellbeing states.
Journal
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Publication Name
ICONIP 2015: 22rd International Conference on Neural Information Processing
Volume
9489
ISBN/ISSN
978-3-319-26555-1
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Issue
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Pages Count
8
Location
Istanbul, Turkey
Publisher
Springer
Publisher Url
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Publisher Location
Berlin, Germany
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Date
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EISSN
N/A
DOI
10.1007/978-3-319-26555-1