Visual content has the ability to convey and impact human emotions. It is crucial to understanding the emotions being communicated and the ways in which they are implied by the visual elements in images. This study evaluates the aesthetic emotion of portrait art generated by our Generative AI Portraiture System. Using the Visual Aesthetic Wheel of Emotion (VAWE), aesthetic responses were documented and subsequently analyzed using heatmaps and circular histograms with the aim of identifying the emotions evoked by the generated portrait art. The data from 160 participants were used to categorize and validate VAWE’s 20 emotions with selected AI portrait styles. The data were then used in a smaller self-portrait qualitative study to validate the developed prototype for an Emotionally Aware Portrait System, capable of generating a personalized stylization of a user’s self-portrait, expressing a particular aesthetic emotional state from VAWE. The findings bring forth a new vision towards blending affective computing with computational creativity and enabling generative systems with awareness in terms of the emotions they wish their output to elicit.