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Full-Text Articles in Physical Sciences and Mathematics
Covariance Pooling For Facial Expression Recognition, D. Acharya, Zhiwu Huang, D. Paudel, Gool L. Van
Covariance Pooling For Facial Expression Recognition, D. Acharya, Zhiwu Huang, D. Paudel, Gool L. Van
Research Collection School Of Computing and Information Systems
Classifying facial expressions into different categories requires capturing regional distortions of facial landmarks. We believe that second-order statistics such as covariance is better able to capture such distortions in regional facial features. In this work, we explore the benefits of using a manifold network structure for covariance pooling to improve facial expression recognition. In particular, we first employ such kind of manifold networks in conjunction with traditional convolutional networks for spatial pooling within individual image feature maps in an end-to-end deep learning manner. By doing so, we are able to achieve a recognition accuracy of 58.14% on the validation set …
Cast2face: Assigning Character Names Onto Faces In Movie With Actor-Character Correspondence, Guangyu Gao, Mengdi Xu, Jialie Shen, Huangdong Ma, Shuicheng Yan
Cast2face: Assigning Character Names Onto Faces In Movie With Actor-Character Correspondence, Guangyu Gao, Mengdi Xu, Jialie Shen, Huangdong Ma, Shuicheng Yan
Research Collection School Of Computing and Information Systems
Automatically identifying characters in movies has attracted researchers' interest and led to several significant and interesting applications. However, due to the vast variation in character appearance as well as the weakness and ambiguity of available annotation, it is still a challenging problem. In this paper, we investigate this problem with the supervision of actor-character name correspondence provided by the movie cast. Our proposed framework, namely, Cast2Face, is featured by: 1) we restrict the assigned names within the set of character names in the cast; 2) for each character, by using the corresponding actor and movie name as keywords, we retrieve …
Face Recognition On Large-Scale Video In The Wild With Hybrid Euclidean-And-Riemannian Metric Learning, Zhiwu Huang, R. Wang, S. Shan, X Chen
Face Recognition On Large-Scale Video In The Wild With Hybrid Euclidean-And-Riemannian Metric Learning, Zhiwu Huang, R. Wang, S. Shan, X Chen
Research Collection School Of Computing and Information Systems
Face recognition on large-scale video in the wild is becoming increasingly important due to the ubiquity of video data captured by surveillance cameras, handheld devices, Internet uploads, and other sources. By treating each video as one image set, set-based methods recently have made great success in the field of video-based face recognition. In the wild world, videos often contain extremely complex data variations and thus pose a big challenge of set modeling for set-based methods. In this paper, we propose a novel Hybrid Euclidean-and-Riemannian Metric Learning (HERML) method to fuse multiple statistics of image set. Specifically, we represent each image …
Cross-View Graph Embedding, Zhiwu Huang, S. Shan, H. Zhang, S. Lao, X. Chen
Cross-View Graph Embedding, Zhiwu Huang, S. Shan, H. Zhang, S. Lao, X. Chen
Research Collection School Of Computing and Information Systems
Recently, more and more approaches are emerging to solve the cross-view matching problem where reference samples and query samples are from different views. In this paper, inspired by Graph Embedding, we propose a unified framework for these cross-view methods called Cross-view Graph Embedding. The proposed framework can not only reformulate most traditional cross-view methods (e.g., CCA, PLS and CDFE), but also extend the typical single-view algorithms (e.g., PCA, LDA and LPP) to cross-view editions. Furthermore, our general framework also facilitates the development of new cross-view methods. In this paper, we present a new algorithm named Cross-view Local Discriminant Analysis (CLODA) …
Cast2face: Character Identification In Movie With Actor-Character Correspondence, Mengdi Xu, Xiaotong Yuan, Jialie Shen, Shuicheng Yan
Cast2face: Character Identification In Movie With Actor-Character Correspondence, Mengdi Xu, Xiaotong Yuan, Jialie Shen, Shuicheng Yan
Research Collection School Of Computing and Information Systems
We investigate the problem of automatically identifying characters in a movie with the supervision of actor-character name correspondence provided by the movie cast. Our proposed framework, namely Cast2Face, is featured by: (i) we restrict the names to assign within the set of character names in the cast; (ii) for each character, by using the corresponding actor's name as a key word, we retrieve from Google image search a group of face images to form the gallery set; and (iii) the probe face tracks in the movie are then identified as one of the actors by robust multi-task joint sparse representation …
Bayesian Tensor Approach For 3-D Face Modeling, Dacheng Tao, Mingli Song, Xuelong Li, Jialie Shen, Jimeng Sun, Xindong Wu, Christos Faloutsos, Stephen J. Maybank
Bayesian Tensor Approach For 3-D Face Modeling, Dacheng Tao, Mingli Song, Xuelong Li, Jialie Shen, Jimeng Sun, Xindong Wu, Christos Faloutsos, Stephen J. Maybank
Research Collection School Of Computing and Information Systems
Effectively modeling a collection of three-dimensional (3-D) faces is an important task in various applications, especially facial expression-driven ones, e.g., expression generation, retargeting, and synthesis. These 3-D faces naturally form a set of second-order tensors-one modality for identity and the other for expression. The number of these second-order tensors is three times of that of the vertices for 3-D face modeling. As for algorithms, Bayesian data modeling, which is a natural data analysis tool, has been widely applied with great success; however, it works only for vector data. Therefore, there is a gap between tensor-based representation and vector-based data analysis …