Entity

Time filter

Source Type


Wang S.,Hefei University of Technology | Wang S.,Key Laboratory of Computing and Communicating Software of Anhui Province | He M.,Hefei University of Technology | He M.,Key Laboratory of Computing and Communicating Software of Anhui Province | And 6 more authors.
Frontiers of Computer Science | Year: 2015

For many supervised learning applications, additional information, besides the labels, is often available during training, but not available during testing. Such additional information, referred to the privileged information, can be exploited during training to construct a better classifier. In this paper, we propose a Bayesian network (BN) approach for learning with privileged information. We propose to incorporate the privileged information through a three-node BN. We further mathematically evaluate different topologies of the three-node BN and identify those structures, through which the privileged information can benefit the classification. Experimental results on handwritten digit recognition, spontaneous versus posed expression recognition, and gender recognition demonstrate the effectiveness of our approach. © 2014, Higher Education Press and Springer-Verlag Berlin Heidelberg. Source


Wang S.,Hefei University of Technology | Wang S.,Key Laboratory of Computing and Communicating Software of Anhui Province | He M.,Hefei University of Technology | He M.,Key Laboratory of Computing and Communicating Software of Anhui Province | And 5 more authors.
Frontiers of Computer Science | Year: 2014

Facial expression and emotion recognition from thermal infrared images has attracted more and more attentions in recent years. However, the features adopted in current work are either temperature statistical parameters extracted from the facial regions of interest or several hand-crafted features that are commonly used in visible spectrum. Till now there are no image features specially designed for thermal infrared images. In this paper, we propose using the deep Boltzmann machine to learn thermal features for emotion recognition from thermal infrared facial images. First, the face is located and normalized from the thermal infrared images. Then, a deep Boltzmann machine model composed of two layers is trained. The parameters of the deep Boltzmann machine model are further fine-tuned for emotion recognition after pre-training of feature learning. Comparative experimental results on the NVIE database demonstrate that our approach outperforms other approaches using temperature statistic features or hand-crafted features borrowed from visible domain. The learned features from the forehead, eye, and mouth are more effective for discriminating valence dimension of emotion than other facial areas. In addition, our study shows that adding unlabeled data from other database during training can also improve feature learning performance. © 2014 Higher Education Press and Springer-Verlag Berlin Heidelberg. Source


Wang S.,Hefei University of Technology | Wang S.,Key Laboratory of Computing and Communicating Software of Anhui Province | He S.,Hefei University of Technology | He S.,Key Laboratory of Computing and Communicating Software of Anhui Province | And 4 more authors.
Frontiers of Computer Science | Year: 2014

Most present research into facial expression recognition focuses on the visible spectrum, which is sensitive to illumination change. In this paper, we focus on integrating thermal infrared data with visible spectrum images for spontaneous facial expression recognition. First, the active appearance model AAM parameters and three defined head motion features are extracted from visible spectrum images, and several thermal statistical features are extracted from infrared (IR) images. Second, feature selection is performed using the F-test statistic. Third, Bayesian networks BNs and support vector machines SVMs are proposed for both decision-level and feature-level fusion. Experiments on the natural visible and infrared facial expression (NVIE) spontaneous database show the effectiveness of the proposed methods, and demonstrate thermal IR images' supplementary role for visible facial expression recognition. © 2014 Higher Education Press and Springer-Verlag Berlin Heidelberg. Source

Discover hidden collaborations