ESTIMATION OF AFFECTIVE VALENCE AND AROUSAL WITH AUTOMATIC FACIAL EXPRESSION MEASUREMENT
Apparatus, methods, and articles of manufacture facilitate analysis of a person's affective valence and arousal. A machine learning classifier is trained using training data created by (1) exposing individuals to eliciting stimuli, (2) recording extended facial expression appearances of the individuals when the individuals are exposed to the eliciting stimuli, and (3) obtaining ground truth of valence and arousal evoked from the individuals by the eliciting stimuli. The classifier is thus trained to analyze images with extended facial expressions (such as facial expressions, head poses, and/or gestures) evoked by various stimuli or spontaneously obtained, to estimate the valence and arousal of the persons in the images. The classifier may be deployed in sales kiosks, online trough mobile and other devices, and in other settings.
1 . A computer-implemented method comprising steps of:
obtaining a first image containing an extended facial expression of a person;
processing the first image containing the extended facial expression of the person with a machine learning classifier to obtain a first estimate of valence and arousal of the person in the first image, wherein the classifier is trained using training data created by (1) recording extended facial expression appearances of individuals, and (2) obtaining ground truths of valence and arousal of the individuals, the ground truths corresponding to the extended facial expression appearances.
2 . A computer-implemented method as in claim 1 , wherein the first image is of the person engaged in spontaneous behavior.
3 . A computer-implemented method as in claim 1 , further comprising:
presenting a first eliciting stimulus to the person, the extended facial expression of the person in the first image being in response to the first eliciting stimulus.
4 . A computer-implemented method as in claim 3 , further comprising:
selecting a second eliciting stimulus for the person based at least in part on the first estimate of valence and arousal of the person; and
presenting to the person the second eliciting stimulus.
5 . A computer-implemented method as in claim 3 , wherein the first eliciting stimulus comprises a first advertisement, the method further comprising:
presenting to the person a second advertisement;
obtaining a second image containing an extended facial expression of the person responding to the second advertising;
processing the second image containing the extended facial expression of the person responding to the second advertising with the machine learning classifier to obtain a second estimate of valence and arousal of the person;
comparing the first estimate and the second estimate; and
indicating a result of the step of comparing, the step of indicating comprising at least one of storing the result of the step of comparing, transmitting the result of the step of comparing, and displaying the result of the step of comparing.
6 . A computer-implemented method as in claim 5 , wherein the first image is obtained at the time when the person observes a first part of a video, and the second image is obtained when the person observes a second part of the video, the method further comprising:
displaying timeline of the video with the first estimate placed nearer time of the first part of the video than time of the second part of the video, and the second estimate placed nearer time of the second part of the video than time of the first part of the video.
7 . A computer-implemented method as in claim 5 , further comprising repeating the method for another person.
8 . A computer-implemented method as in claim 4 , wherein the step of selecting comprises identifying the second eliciting stimulus from a function mapping a plurality of possible estimates of valence and arousal evoked by the first eliciting stimulus to a plurality of selections available for the second eliciting stimulus, wherein the function is developed using one or more machine learning methods.
9 . A computer-implemented method as in claim 4 , wherein the step of selecting comprises identifying the second eliciting stimulus from a function mapping a plurality of possible estimates of valence and arousal evoked by the first eliciting stimulus in conjunction with demographic data, to a plurality of selections available for the second eliciting stimulus, wherein the function is developed using a method selected from the group consisting of reinforcement learning and optimal control methods.
10 . A computer-implemented method as in claim 4 , further comprising:
step for selecting a second eliciting stimulus for the person based at least in part on the first estimate of valence and arousal of the person responding to the first eliciting stimulus; and
presenting to the person the second eliciting stimulus.
11 . A computer-implemented method comprising:
training a machine learning classifier using training data created by (1) recording extended facial expression appearances of individuals when the individuals, and (3) obtaining ground truths of valence and arousal of the individuals, the ground truths corresponding to the extended facial expression appearances of the individuals, thereby obtaining a machine learning classifier trained to estimate valence and arousal.
12 . A computer-implemented method as in claim 11 , further comprising:
processing an image of a person with the classifier to generate an estimate of valence and arousal of the person.
13 . A computing device comprising:
at least one processor;
machine-readable storage, the machine-readable storage being coupled to the at least one processor, the machine-readable storage storing instructions executable by the at least one processor; and
means for allowing the at least one processor to obtain images comprising extended facial expressions of a person;
wherein:
the instructions, when executed by the at least one processor, configure the at least one processor to implement a machine learning classifier trained to estimate valence and arousal, wherein the classifier is trained with training data created by (1) recording extended facial expression appearances of individuals, and (2) obtaining ground truths of valence and arousal, the ground truths corresponding to the extended facial expression appearances of the individuals; and
the instructions, when executed by the at least one processor, further configure the at least one processor to analyze a first image comprising an extended facial expression of the person, using the classifier, thereby obtaining an estimate of valence and arousal of the person.
14 . A computing device as in claim 13 , further comprising:
means for presenting a first eliciting stimulus to the person.
15 . A computing device as in claim 14 , wherein:
the means for allowing the at least one processor to obtain images comprises a camera of the computing device; and
the means for presenting comprises a display of the computing device.
16 . A computing device as in claim 14 , wherein:
the means for allowing the at least one processor to obtain images comprises a network interface coupling through a network the computing device to a user device; and
the means for presenting comprises the network interface.
17 . A computing device as in claim 13 , wherein the instructions, when executed by the at least one processor, further configure the at least one processor to select a second eliciting stimulus based at least in part on the estimate of valence and arousal of the person responding to the first eliciting stimulus.
18 . A computing device as in claim 17 , further comprising:
means for presenting the first eliciting stimulus and the second eliciting stimulus to the person.
19 . A computing device as in claim 18 , wherein:
the means for allowing the at least one processor to obtain images comprises a network interface coupling the computing device through a network to a user device;
the means for presenting comprises the network interface;
wherein:
the computing device is configured to present the first eliciting stimulus and the second eliciting stimulus by sending signals to the user device through the network interface; and
the computing device is configured to obtain the images by receiving signals from the user device through the network interface.
20 . A computer-implemented method comprising steps of:
obtaining a plurality of images containing extended facial expressions of a plurality of persons at a plurality of times;
processing the plurality of images with a machine learning classifier to obtain a plurality of estimates of valence and arousal of the plurality of persons, wherein the classifier is trained using training data created by (1) recording extended facial expression appearances of individuals, and (2) obtaining ground truths of valence and arousal of the individuals, the ground truths corresponding to the extended facial expression appearances;
computing statistics of valence and arousal of the plurality of persons over time; and
at least one of storing, displaying, and transmitting the statistics.