Facial reshaping operator for controllable face beautification

Posting attractive facial photos is part of everyday life in the social media era. Motivated by the demand, we propose a lightweight method to automatically and efficiently beautify the shapes of both portrait and non-portrait faces in photos, while allowing users to customize the beautification of individual facial features. Previous methods focus on the beautification of mostly frontal and neutral faces, without incorporating user controllability in the beautification process. To address these restrictions, we propose the Facial Reshaping Operator representation, which is affine-invariant, captures the pairwise geometric configuration of facial landmarks, and allows for efficient face beautification with the user-specified weights of individual facial parts. We also propose an unsupervised beautification method in the operator space of faces, where an input face is iteratively pulled towards a local nearby density mode with improved attractiveness. Our method distinguishes itself from the commercial beautification tools in that it mildly enhances facial shapes without altering makeups or complexions, which complements these tools that lack fine-grained control on the attractiveness of facial shapes for users. The experimental results show that our method improves facial shape attractiveness for a large range of poses and expressions, demonstrating the potential of applicability to photos seen on the social media such as Facebook and Instagram everyday

A methodology for automatic parameter-tuning and center selection in density-peak clustering methods

The density-peak clustering algorithm, which we refer to as DPC, is a novel and efficient density-based clustering approach. The method has the advantage of allowing non-convex clusters, and clusters of variable size and density, to be grouped together, but it also has some limitations, such as the visual location of centers and the parameter tuning. This paper describes an optimization-based methodology for automatic parameter/center selection applicable both to the DPC and to other algorithms derived from it. The objective function is an internal/external cluster validity index, and the decisions are the parameterization of the algorithm and the choice of centers. The internal validation measures lead to an automatic parameter-tuning process, and the external validation measures lead to the so-called optimal rules, which are a tool to bound the performance of a given algorithm from above on the set of parameterizations. A numerical experiment with real data was performed for the DPC and for the fuzzy weighted k-nearest neighbor (FKNN-DPC) which validates the automatic parameter-tuning methodology and demonstrates its efficiency compared to the state of the art.El algoritmo de agrupamiento de picos de densidad, al que nos referimos como DPC , es un enfoque de agrupamiento basado en la densidad novedoso y eficiente. El método tiene la ventaja de permitir agrupar clústeres no convexos y clústeres de tamaño y densidad variables, pero también tiene algunas limitaciones, como la ubicación visual de los centros y el ajuste de parámetros. Este artículo describe una metodología basada en la optimización para la selección automática de parámetros/centros aplicable tanto al DPC como a otros algoritmos derivados de él. La función objetivo es un índice de validez de clúster interno/externo, y las decisiones son la parametrización del algoritmo y la elección de los centros. Las medidas de validación interna conducen a un proceso automático de ajuste de parámetros, y las medidas de validación externa conducen al llamadoreglas óptimas , que son una herramienta para limitar el rendimiento de un algoritmo dado desde arriba en el conjunto de parametrizaciones. Se realizó un experimento numérico con datos reales para el DPC y para el k -vecino más cercano ponderado difuso ( FKNN-DPC ) que valida la metodología de ajuste automático de parámetros y demuestra su eficiencia en comparación con el estado del arte