Recent advancements in artificial intelligence have significantly impacted many color imaging applications, including skin segmentation and enhancement. Although state-of-the-art methods emphasize geometric image augmentations to improve model performance, the role of color-based augmentations and color spaces in enhancing skin segmentation accuracy remains underexplored. This study addresses this gap by systematically evaluating the impact of various color-based image augmentations and color spaces on skin segmentation models based on convolutional neural networks (CNNs). We investigate the effects of color transformations—including brightness, contrast, and saturation adjustments—in three color spaces: sRGB, YCbCr, and CIELab. To represent CNN models, an existing semantic segmentation model is trained using these color augmentations on a custom dataset of 900 images with annotated skin masks, covering diverse skin tones and lighting conditions. Our findings reveal that current training practices, which primarily rely on single-color augmentation in the sRGB space and focus mainly on geometric augmentations, limit model generalization in color-related applications like skin segmentation. Models trained with a greater variety of color augmentations show improved skin segmentation, particularly under over- and underexposure conditions. Additionally, models trained in YCbCr outperform those trained in sRGB color space when combined with color augmentation while CIELab leads to comparable performance to sRGB. We also observe significant performance discrepancies across skin tones, highlighting challenges in achieving consistent segmentation under varying lighting. This study highlights gaps in existing image augmentation approaches and provides insights into the role of various color augmentations and color spaces in improving the accuracy and inclusivity of skin segmentation models.