Recent advancements in Artificial Intelligence and Computer Vision, in particular Deep Learning (DL), have transformed the analysis of human faces, enabling different tasks, ranging from classification to synthesis. Despite these advancements, color analysis in face images remains underexplored, especially concerning well-defined datasets and frameworks tailored to specific methodologies such as Season Color Analysis or Armocromia. Armocromia combines qualitative and quantitative approaches to determine personal color palettes based on an individual's skin, hair, and eye color; for this, it is vastly adopted in the fashion world. However, we found a lack of datasets to train DL models to automatically discriminate among these classes. To this date, we introduce Deep Armocromia, a novel dataset comprising labeled face images categorized according to Armocromia Flow Theory, with a strict annotation protocol. We conduct experiments to validate the effectiveness of DL models in discriminating among Armocromia classes optimized on Deep Armocromia. Results underscore the challenges inherent to Armocromia classification and highlight opportunities for advancing DL architectures and optimization methodologies.
@inproceedings{stacchio2024deep,
title = {Deep Armocromia: A Novel Dataset for Face Seasonal Color Analysis and Classification},
author = {Lorenzo Stacchio and Marina Paolanti and Francesca Spigarelli and Emanuele Frontoni},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV) Workshops},
year = {2024},
address = {Milan, Italy},
month = {October},
publisher = {Springer},
}