A Novel Rule-Based Skin Detection Method using Principal Component Analysis-Based Dimensionality Reduction and Individual Contribution on Principal Components

Skin detection plays a vital role in various human-related computer vision applications, including human-computer interaction, medical diagnostic tools, and web content filtering. However, accurate skin detection remains challenging due to different factors such as luminosity variations, complex backgrounds, and diversity in skin tones. In this paper we present a rule-based skin detection method that applies dimensionality reduction using Principal Component Analysis (PCA) on pixels represented by multiple color channels. This process retains only the most pertinent information in the form of principal components. Subsequently, skin detection is achieved according to the individual contribution of the pixels along these principal components. To evaluate the effectiveness of our approach, we conducted comprehensive experiments on the SFA dataset, using the Recall, Specificity, F-measure, and D
prs
as evaluation metrics. Our method demonstrated consistently superior skin detection performance compared to other rule-based methods, in both quantitative and qualitative aspects across diverse scenarios.