An empirical study of facial components classification by integrating dimensionality reduction and clustering
In this paper we present an empirical study of facial components classification by integrating dimensionality reduction and unsupervised clustering. The proposed framework contains two iterative steps: 1) Fixing cluster labels, the facial samples are projected onto lower dimensional subspace through dimensionality reduction method; 2) Fixing the subspace, the clustering algorithm is performed to generate cluster labels. Through iterative steps, clusters are discovered in the lower dimensional subspaces to avoid the curse of dimensionality, while the subspaces are adaptively re-adjusted for global optimality. In order to achieve an effective and robust system, we compare the PCA with the LLE for dimensionality reduction, and compare K-means with LDA-guided K-means(LDA-Km) for unsupervised clustering. The quantitative experimental results prove the LLE and LDA-Km are superior for facial data on public Lotus Hill Institute(LHI) dataset. We also apply the presented study to improve the portrait sketching results.