Automatic correction of facial sentiment of portrait images

Abstract

Techniques are disclosed for correcting facial sentiment of digital images. Facial data captured in a target image is analyzed to obtain facial-based sentiment. A favored sentiment is determined based at least in part on the facial-based sentiment. The favored sentiment is then applied to at least one face included in the target image that doesn't reflect the favored sentiment. Analyzing facial data may include detecting facial landmarks that are good indicators of sentiment (e.g., eyes, mouth, eyebrows, jawline, and nose). Such landmarks can be processed, with supervised machine learning, to detect the corresponding facial sentiment. A favored sentiment of the target image is thus identified, and can be applied to one or more non-compliant faces in the target image. In some embodiments, the favored sentiment can be further based on a plurality of additional sentiment indicators, including geo data, text, and/or other images associated with the target image.