Face recognition using sparse feature sphere centroid classifier

The sparse feature sphere centroid (SFSC) classifier for face recognition is proposed. SFSC is based on nearest feature plane (NFP), sparse representation classification (SRC) and nearest feature centres (NFC), and it contains two stages. In the first stage, the SFSC classifier computes the feature sphere centroid metric. Then, SFSC obtains the sparse coefficients by solving an L₁-norm minimisation problem and uses the sparse coefficients to calculate the weighted feature sphere centroid distance, which will be utilised for classification. Experiments on the Georgia Tech (GT) face database and AR face database were conducted to evaluate the proposed classifier. The experimental results show that the proposed classifier yields better recognition rate over competing classifiers such as NFC, NFP and SRC.