Clustering an African Hairstyle Dataset using PCA and K-Means


Teffo Phomolo Nicrocia, Owolawi Pius Adewale, Pholo Moanda Diana, Tshwane University of Technology, Pretoria (Soshanguve)


The adoption of digital transformation was not expressed in building an African face shape classifier. In this paper, an approach is presented that uses k-means to classify African women images. African women rely on beauty standards recommendations, personal preference, or the newest trends in hairstyles to decide on the appropriate hairstyle for them. In this paper, an approach is presented that uses K-means clustering to classify African women's images. In order to identify potential facial clusters, Haarcascade is used for feature-based training, and K-means clustering is applied for image classification.


Face detection, k-means, African, hairstyle