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AI Biotech/Diagnostics: Activity
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Application US20200319706
Published 2020-10-08
System And Method For Detecting Invisible Human Emotion In A Retail Environment
A system for detecting invisible human emotion in a retail environment is provided. The system comprises a camera and an image processing unit. The camera is configured in a retail environment to capture an image sequence of a person before and during when a price of a product or service becomes visible. The image processing unit is trained to determine a set of bitplanes of a plurality of images in the captured image sequence that represent the hemoglobin concentration (HC) changes of the person, and to detect the person's invisible emotional states based on HC changes. The image processing unit is trained using a training set comprising a set of subjects for which emotional state is known.
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- 1. A system for determining probability for a state of human emotion among a set of identifiable states of human emotion from a digital image sequence of a person in a retail environment, the system comprising:
a computer-readable memory comprising the digital image sequence, the digital image sequence being of light re-emitted from the skin of the person before and during viewing of a product; and a processing unit comprising one or more processors in communication with the computer-readable memory, the image processing unit executable to:
determine, using a first machine learning model trained with a hemoglobin concentration (HC) training set, HC changes of the person using bit values from each bitplane of images in the captured image sequence, the HC training set comprising bit values from each bitplane of images captured from one or more people while such people experience a known state of emotion; and
determine a measure of probability, using a second machine learning model trained with a state training set, for the emotional state of the person against each of the set of identifiable states of human emotion, the state training set obtained by receiving bit values from each bitplane of images representing HC changes determined by the first machine learning model.
- 9. A method for determining probability for a state of human emotion among a set of identifiable states of human emotion from a digital image sequence of a person in a retail environment, the digital image sequence being of light re-emitted from the skin of the person before and during viewing of a product, the method comprising:
determining, using a first machine learning model trained with a hemoglobin concentration (HC) training set, HC changes of the person using bit values from each bitplane of images in the captured image sequence, the HC training set comprising bit values from each bitplane of images captured from one or more people while such people experience a known state of emotion; and determinizing a measure of probability, using a second machine learning model trained with a state training set, for the emotional state of the person against each of the set of identifiable states of human emotion, the state training set obtained by receiving bit values from each bitplane of images representing HC changes determined by the first machine learning model.
