A robust SVM classification framework using PSM for multi-class recognition

Our research focuses on the question of classifiers that are capable of processing images rapidly and accurately without having to rely on a large-scale dataset, thus presenting a robust classification framework for both facial expression recognition (FER) and object recognition. The framework is based on support vector machines (SVMs) and employs three key approaches to enhance its robustness. First, it uses the perturbed subspace method (PSM) to extend the range of sample space for task sample training, which is an effective way to improve the robustness of a training system. Second, the framework adopts Speeded Up Robust Features (SURF) as features, which is more suitable for dealing with real-time situations. Third, it introduces region attributes to evaluate and revise the classification results based on SVMs. In this way, the classifying ability of SVMs can be improved.