Computer‐Suggested Facial Makeup

Abstract

Finding the best makeup for a given human face is an art in its own right. Experienced makeup artists train for years to be skilled enough to propose a best-fit makeup for an individual. In this work we propose a system that automates this task. We acquired the appearance of 56 human faces, both without and with professional makeup. To this end, we use a controlled-light setup, which allows to capture detailed facial appearance information, such as diffuse reflectance, normals, subsurface-scattering, specularity, or glossiness. A 3D morphable face model is used to obtain 3D positional information and to register all faces into a common parameterization. We then define makeup to be the change of facial appearance and use the acquired database to find a mapping from the space of human facial appearance to makeup. Our main application is to use this mapping to suggest the best-fit makeup for novel faces that are not in the database. Further applications are makeup transfer, automatic rating of makeup, makeup-training, or makeup-exaggeration. As our makeup representation captures a change in reflectance and scattering, it allows us to synthesize faces with makeup in novel 3D views and novel lighting with high realism. The effectiveness of our approach is further validated in a user-study.

Can facial cosmetics affect the matching accuracy of face recognition systems?

“…The paper by Ueda and Koyama [16] discusses a proprietary database consisting of Japanese females. The work by Scherbaum et al [12] specifies a private database containing 56 subjects, each with and without makeup. However, these databases could not be obtained from the respective researchers.…”

Hallucination of facial details from degraded images using 3D face models

“…While Scherbaum et al [31] applied the makeup of one person to the face of another one, the fundamental idea of our approach is to transfer facial details, so that the formerly low resolution texture T i, L (u, v) of person i gets transformed into a high-resolution texture. In the following, the result of this process is denoted as T i, L → H (u, v).…”

Realtime Dynamic 3D Facial Reconstruction for Monocular Video In-the-Wild

“…In recent years, the wide spread availability of 2D RGB mobile cameras has sparked interest in 3D facial reconstruction from 2D input. Due to the increased interest of casual untrained users in applications such as image, video editing [34,10], virtual makeup [29] and facial model creation [6].…”