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

Abstract

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. Combining these approaches, the proposed method has the following beneficial contributions. First, the efficiency of SVMs can be improved. Experiments show that the proposed approach is capable of reducing the number of samples effectively, resulting in an obvious reduction in training time. Second, the recognition accuracy is comparable to that of state-of-the-art algorithms. Third, its versatility is excellent, allowing it to be applied not only to object recognition but also FER.

Multithreading cascade of SURF for facial expression recognition

“…In contrast, the proposed framework treats feature learning separately by dopting the subject-independent classifier to the final objective of classification. Since local features trained by classifiers can effectively cancel out the problems caused by semantic gap, which leads to an overall significant improvement of the classification performance [37][38][39]. Thus, the learned feature and classifier have specificity and discriminative capability.…”

Deep-learned faces: a survey

“…Since many studies have encouraged the use of subject-specific modeling to better utilize all the available information from multiple visual data [27,[39][40][41]59], it is important to properly analyze the strengths and weaknesses of different modeling approaches. The popularity of SVM in image classification can be explained by its ability to scale well with high dimensional data [131][132][133]. Although this works well when provided with small number of classes, increased number of classes with limited train data per class could complicate the process of finding the separation hyperplane.…”