Face Recognition Based On Improved Support Vector Clustering

Traditional methods for face recognition do not scale well with the number of training sample, which limits the wide applications of related techniques. We propose an improved Support Vector Clustering algorithm to handle the large-scale biometric feature data effectively. We prove theoretically that the proposed algorithm converges to the optimum within any given precision quickly. Compared to related state-of-the-art Support Vector Clustering algorithms, it has the competitive performances on both training time and accuracy. Besides, we use the proposed algorithm to handle classification problem, and face recognition, as well. Experiments on synthetic and real-world data sets demonstrate the validity of the proposed algorithm.