Guided Selfies using Models of Portrait Aesthetics
Qifan Li, Daniel Vogel · 2017
We introduce techniques enabling interactive guidance for better self-portrait photos ("selfies") using a smartphone cam- era. Aesthetic quality is estimated using empirical models for three parameterized composition principles: face size, face position, and lighting direction. The models are built using 2,700 crowdworker assessments of highly-controlled synthetic selfies. These are generated by manipulating a virtual camera and lighting when rendering a realistic 3D model of a human to methodically explore the parameter space. A camera application uses the models to estimate the aesthetic quality of a live selfie preview based on parameters measured by computer vision. The photographer is guided towards a better selfie by directional hints overlaid on the live preview. A study shows the technique provides a 26% increase in aesthetic quality compared to a standard camera application.