2. A computer-implemented method as in claim 1 , further comprising: training a machine-learning classifier of an emotion, affective state, or action unit associated with the predetermined expression using the one or more images that passed the second quality check; and using the classifier, after the step of training, to classify new images that have not been used in training of the classifier; wherein the images that fail the first quality check are not sent to the one or more experts.
2. A computer-implemented method as in claim 1 , further comprising: training a machine-learning classifier of an emotion, affective state, or action unit associated with the predetermined expression using the one or more images that passed the second quality check; and using the classifier, after the step of training, to classify new images that have not been used in training of the classifier; wherein the images that fail the first quality check are not sent to the one or more experts. 9. A computer-implemented method as in claim 2 , further comprising: selecting from the one or more images that passed the second quality check discrepant images that cause a discrepancy between labels assigned by the second plurality of untrained workers and labels assigned by the machine-learning classifier; sending the discrepant images to the second plurality of untrained providers; receiving ratings of the discrepant images from the second plurality of untrained providers; applying the first quality check to the discrepant images rated by the second plurality of untrained providers, the step of applying the first quality check to the discrepant images resulting in one or more discrepant images that passed the first quality check; sending the one or more discrepant images that passed the first quality check to the one or more experts, for rating by the one or more experts; in response to the step of sending the one or more discrepant images that passed the first quality check, receiving one or more ratings of discrepant from the one or more experts; and applying the second quality check to the one or more discrepant images rated by the one or more experts; training the classifier with the one or more discrepant images that passed the second quality check; and using the classifier, after the step of training the classifier with the one or more discrepant images, to classify additional new images that have not been used in training of the classifier.