Edge-Aware Label Propagation for Mobile Facial Enhancement on the Cloud

This paper proposes a facial enhancement framework with mask generation for cloud-based mobile applications. We mathematically analyze and unify the mask generation, as well as the state-of-the-art region-aware mask and edit propagation techniques, from a graph-based semi supervised learning perspective. Then we propose a label propagation model with a new edge-aware structure and guided feature for mask generation. The limit analysis of the model leads to a fast algorithm, which reduces the intrinsic computation cost. Then we develop a flexible and efficient cloud-based PaaS system, called FaceMore, for intelligent mobile face enhancement. The flexibility and extendibility of the cloud-based architecture facilitates intelligent facial enhancement applications and the parallel processing effectively improves the efficiency of the algorithm. Qualitative and quantitative evaluations were performed for mask propagation and face enhancement. Comparisons with the previous methods and five representative commercial systems, including PicTreat, Portraiture, Portrait+, Meitu, and Baidu Motu, illustrate the robustness and effectiveness of our method.