Facial skin beautification using adaptive region-aware masks

Area of Science:

• Computer Vision
• Image Processing
• Digital Photography

Background:

• Facial beautification requires precise control over skin homogeneity, lighting, and color.
• Existing methods often lack precision and flexibility in image manipulation.
• Automated region selection and precise editing are crucial for effective facial enhancement.

Purpose of the Study:

• To propose a unified facial beautification framework.
• To develop a novel region-aware mask for accurate skin manipulation.
• To enhance skin homogeneity, lighting, and color with improved precision and flexibility.

Main Methods:

• A region-aware mask is constructed using an adaptive edge-preserving energy minimization model and a high-dimensional guided feature space.
• Portraits are decomposed into smoothness, lighting, and color layers using an edge-preserving operator.
• Facial landmarks and features are extracted as constraints for mask generation, enabling user-driven parameter adjustment.

Main Results:

• The proposed method achieves accurate facial skin enhancement with simplified complex manipulations.
• Region-aware masks facilitate flexible and precise editing of skin properties.
• Qualitative and quantitative evaluations demonstrate superior performance compared to previous methods and comparability to commercial systems.

Conclusions:

• The unified framework offers a significant advancement in automated facial beautification.
• The region-aware mask technology enables precise and user-friendly skin enhancement.
• The method shows broad applicability across diverse facial demographics and image conditions.