Systems and methods for skin color classification

A method for classification of skin color, the method comprising:

• i) receiving first data of at least one image of skin of a subject;
• ii) transforming the at least one image of the skin of the subject from an original color space to a standardized color space to give standardized data;
• iii) using an unsupervised learning model to perform clustering and matching on the standardized data to give clustered data;
• and
• iv) matching cluster centers from the clustered data to cluster centers of a Fitzpatrick skin type (FST) to classify each image of the skin of the subject into an FST color,
• step iii) comprising resizing images of the standardized data, reshaping the images of the standardized data, and iteratively applying the unsupervised learning model until the clustered data is obtained, and
• step iv) comprising calculating color distances, between the cluster centers from the clustered data and the cluster centers of the FST, using a colorimetry metric, followed by matching each cluster center from the clustered data with a nearest cluster center of the FST based on the calculated color distances.

A system for classification of skin color, the system comprising:

• a processor;
• a display in operable communication with the processor;
• and
• a machine-readable medium in operable communication with the processor and having instructions stored thereon that, when executed by the processor, perform the following steps:
• i) receiving first data of at least one image of skin of a subject;
• ii) transforming the at least one image of the skin of the subject from an original color space to a standardized color space to give standardized data;
• iii) using an unsupervised learning model to perform clustering and matching on the standardized data to give clustered data;
• iv) matching cluster centers from the clustered data to cluster centers of a Fitzpatrick skin type (FST) to classify each image of the skin of the subject into an FST color;
• v) generating a visualization of colors from the at least one image of the skin of the subject with corresponding respective FST colors;
• vi) displaying the visualization on the display;
• and
• vii) providing the classification of each image to a clinician to compare to ground truth classifications of each image obtained from a ground truth device,
• the standardized color space being LAB color space,
• the subject being a human subject,
• the unsupervised learning model being K-means,
• step iii) comprising resizing images of the standardized data, reshaping the images of the standardized data, and iteratively applying the unsupervised learning model until the clustered data is obtained, and
• step iv) comprising calculating color distances, between the cluster centers from the clustered data and the cluster centers of the FST, using a colorimetry metric, followed by matching each cluster center from the clustered data with a nearest cluster center of the FST based on the calculated color distances.