clustering an african hairstyle dataset using pca and k-means
This work addresses a domain-specific problem for African women seeking hairstyle recommendations, but it is incremental as it applies existing methods to new data.
The paper tackled the lack of an African face shape classifier by using K-means clustering on African women's images to classify hairstyles, achieving image classification through feature-based training with Haarcascade.
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.