Locality oriented feature extraction for small training datasets using non-negative matrix factorization
This paper proposes a simple and effective method to construct descriptive features for partially occluded face image recognition. This method is aimed for any small dataset which contains only one or two training images per subject, namely Locality oriented feature extraction for small training datasets (LOFESS). In this method, gallery images are first partitioned into sub-regions excluding obstructed parts to generate a collection of initial basis vectors. Then these vectors are trained with Non-negative matrix factorization algorithm to find part-based bases. These bases finally build up a local occlusion-free feature space. The main contribution in this paper is the incorporation of locality information into LOFESS bases to preserve spatial facial structure. The presented method is applied to recognize disguised faces wearing sunglasses or scarf in a control environment without any alignment required. Experimental results on the Aleix-Robert database show the effectiveness of the LOFESS method.
non-negative matrix factorization disguided face recognition partial occluded face recognition alignment free face recognition