A two-stage classifier using SVM and RANSAC for face recognition
Chen-Hui Kuo, Jiann-Der Lee · 2007
A novel face recognition scheme based on twostage classifier, which includes methods of support vector machine (SVM), and random sample consensus (RANSAC), is proposed in this paper. The whole decision process is undertaken by cascade stages. The first stage with OAO-SVM (one-against-one) method picks out two classes with the least variations to the testing images. From the selected two classes, the second stage with “RANSAC” method is used for a fine match with testing images. A fine class with greatest geometric similarity to testing images is thus produced at second stage. This two-stage face recognition system has been tested on Olivetti Research Laboratory (ORL) databases, and the experimental results give evidence that the proposed approach is superior to the previous approaches based on the single classifier and multi-parallel classifier in recognition accuracy.