Abstract: Apparatus, methods, and articles of manufacture for implementing crowdsourcing pipelines that generate training examples for machine learning expression classifiers. Crowdsourcing providers actively generate images with expressions, according to cues or goals. The cues or goals may be to mimic an expression or appear in a certain way, or to “break” an existing expression recognizer. The images are collected and rated by same or different crowdsourcing providers, and the images that meet a first quality criterion are then vetted by expert(s). The vetted images are then used as positive or negative examples in training machine learning expression classifiers.

Abstract: Apparatus, methods, and articles of manufacture for implementing crowdsourcing pipelines that generate training examples for machine learning expression classifiers. Crowdsourcing providers actively generate images with expressions, according to cues or goals. The cues or goals may be to mimic an expression or appear in a certain way, or to “break” an existing expression recognizer. The images are collected and rated by same or different crowdsourcing providers, and the images that meet a first quality criterion are then vetted by expert(s). The vetted images are then used as positive or negative examples in training machine learning expression classifiers.

Abstract: Apparatus, methods, and articles of manufacture for implementing crowdsourcing pipelines that generate training examples for machine learning expression classifiers. Crowdsourcing providers actively generate images with expressions, according to cues or goals. The cues or goals may be to mimic an expression or appear in a certain way, or to “break” an existing expression recognizer. The images are collected and rated by same or different crowdsourcing providers, and the images that meet a first quality criterion are then vetted by expert(s). The vetted images are then used as positive or negative examples in training machine learning expression classifiers.

Abstract: Apparatus, methods, and articles of manufacture for obtaining examples that break a visual expression classifier at user devices such as tablets, smartphones, personal computers, and cameras. The examples are sent from the user devices to a server. The server may use the examples to update the classifier, and then distribute the updated classifier code and/or updated classifier parameters to the user devices. The users of the devices may be incentivized to provide the examples that break the classifier, for example, by monetary reward, access to updated versions of the classifier, public ranking or recognition of the user, a self-rewarding game. The examples may be evaluated using a pipeline of untrained crowdsourcing providers and trained experts. The examples may contain user images and/or depersonalized information extracted from the user images.

Abstract: An automatic facial action coding system and method can include processing an image to identify a face in the image, to detect and align one or more facial features shown in the image, and to define one or more windows on the image. One or more distributions of pixels and color intensities can be quantified in each of the one or more windows to derive one or more two-dimensional intensity distributions of one or more colors within the window. The one or more two-dimensional intensity distributions can be processed to select image features appearing in the one or more windows and to classify one or more predefined facial actions on the face in the image. A facial action code score that includes a value indicating a relative amount of the predefined facial action occurring in the face in the image can be determined for the face in the image for each of the one or more predefined facial actions.

Abstract: An automatic facial action coding system and method can include processing an image to identify a face in the image, to detect and align one or more facial features shown in the image, and to define one or more windows on the image. One or more distributions of pixels and color intensities can be quantified in each of the one or more windows to derive one or more two-dimensional intensity distributions of one or more colors within the window. The one or more two-dimensional intensity distributions can be processed to select image features appearing in the one or more windows and to classify one or more predefined facial actions on the face in the image. A facial action code score that includes a value indicating a relative amount of the predefined facial action occurring in the face in the image can be determined for the face in the image for each of the one or more predefined facial actions.

Abstract: An automatic facial action coding system and method can include processing an image to identify a face in the image, to detect and align one or more facial features shown in the image, and to define one or more windows on the image. One or more distributions of pixels and color intensities can be quantified in each of the one or more windows to derive one or more two-dimensional intensity distributions of one or more colors within the window. The one or more two-dimensional intensity distributions can be processed to select image features appearing in the one or more windows and to classify one or more predefined facial actions on the face in the image. A facial action code score that includes a value indicating a relative amount of the predefined facial action occurring in the face in the image can be determined for the face in the image for each of the one or more predefined facial actions.