Gaussian hamming distance: de-identified features of facial expressions

Song, Insu (2015) Gaussian hamming distance: de-identified features of facial expressions. In: Lecture Notes in Computer Science (9489), pp. 233-240. From: ICONIP 2015: 22rd International Conference on Neural Information Processing, 9-12 November 2015, Istanbul, Turkey.

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Abstract

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.

Item ID: 40074
Item Type: Conference Item (Refereed Research Paper - E1)
Keywords: Gaussian hamming distance, general well-being, facial feature, feature selection, mobile diagnostics
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ISBN: 978-3-319-26555-1
Date Deposited: 02 Jun 2016 02:24
FoR Codes: 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining @ 100%
SEO Codes: 92 HEALTH > 9202 Health and Support Services > 920203 Diagnostic Methods @ 100%
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