Geometric Feature Enhancement for Robust Facial Landmark Detection in Makeup Paper Templates
Source abstract
Abstract
Traditional scoring of makeup face templates in beauty skill assessments heavily relies on manual judgment, leading to inconsistencies and subjective bias. Hand-drawn templates often exhibit proportion distortions, asymmetry, and occlusions that reduce the accuracy of conventional facial landmark detection algorithms. This study proposes a novel approach that integrates Geometric Feature Enhancement (GFE) with Dlib’s 68-landmark detection to improve the robustness and precision of landmark localization. A comprehensive comparison among Haar Cascade, MTCNN-MobileNetV2, and Dlib was conducted using a curated dataset of 11,600 hand-drawn facial templates. The proposed GFE-enhanced Dlib achieved 60.5% accuracy—outperforming MTCNN (23.4%) and Haar (20.3%) by approximately 37 percentage points, with precision and F1-score improvements exceeding 20% and 25%, respectively. The results demonstrate that the proposed method significantly enhances detection accuracy and scoring consistency, providing a reliable framework for automated beauty skill evaluation, and laying a solid foundation for future applications such as digital archiving and style-guided synthesis.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Face recognition and analysis · Evolutionary Psychology and Human Behavior · Face Recognition and Perception
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Graduate Institute of Applied Science and Engineering, Fu Jen Catholic University, New Taipei City 243062, Taiwan
Library and Information Center, Lee-Ming Institute of Technology, New Taipei City 243083, Taiwan
Department of Computer Science and Information Engineering, Fu Jen Catholic University, New Taipei City 242062, Taiwan
Funding
National Science and Technology Council
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