Texture features for clustering based multi-label classification of face images
Ahmed Abdulateef Mohammed, Reziwanguli Xiamixiding, Atul Sajjanhar, Juan Chen, Gulisong Nasierding · 2017
In this paper, we propose approaches for representation of texture features to improve classification of face images. The proposed methods for texture representation use Local Binary Pattern based approaches on polar raster sampled face images. Face images from FERET and CK+ databases are classified using clustering for multi-label classification. The proposed methods for texture representation show an improvement for clustering-based multi-label classification of face images.