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Full-Text Articles in Physical Sciences and Mathematics
High-Resolution Face Swapping Via Latent Semantics Disentanglement, Yangyang Xu, Bailin Deng, Junle Wang, Yanqing Jing, Jia Pan, Shengfeng He
High-Resolution Face Swapping Via Latent Semantics Disentanglement, Yangyang Xu, Bailin Deng, Junle Wang, Yanqing Jing, Jia Pan, Shengfeng He
Research Collection School Of Computing and Information Systems
We present a novel high-resolution face swapping method using the inherent prior knowledge of a pre-trained GAN model. Although previous research can leverage generative priors to produce high-resolution results, their quality can suffer from the entangled semantics of the latent space. We explicitly disentangle the latent semantics by utilizing the progressive nature of the generator, deriving structure at-tributes from the shallow layers and appearance attributes from the deeper ones. Identity and pose information within the structure attributes are further separated by introducing a landmark-driven structure transfer latent direction. The disentangled latent code produces rich generative features that incorporate feature blending …
Generating Face Images With Attributes For Free, Yaoyao Liu, Qianru Sun, He Xiangnan, Liu An-An, Su Yuting, Chua Tat-Seng
Generating Face Images With Attributes For Free, Yaoyao Liu, Qianru Sun, He Xiangnan, Liu An-An, Su Yuting, Chua Tat-Seng
Research Collection School Of Computing and Information Systems
With superhuman-level performance of face recognition, we are more concerned about the recognition of fine-grained attributes, such as emotion, age, and gender. However, given that the label space is extremely large and follows a long-tail distribution, it is quite expensive to collect sufficient samples for fine-grained attributes. This results in imbalanced training samples and inferior attribute recognition models. To this end, we propose the use of arbitrary attribute combinations, without human effort, to synthesize face images. In particular, to bridge the semantic gap between high-level attribute label space and low-level face image, we propose a novel neural-network-based approach that maps …
Amora: Black-Box Adversarial Morphing Attack, Run Wang, Felix Juefei-Xu, Qing Guo, Yihao Huang, Xiaofei Xie, Lei Ma, Yang Liu
Amora: Black-Box Adversarial Morphing Attack, Run Wang, Felix Juefei-Xu, Qing Guo, Yihao Huang, Xiaofei Xie, Lei Ma, Yang Liu
Research Collection School Of Computing and Information Systems
Nowadays, digital facial content manipulation has become ubiquitous and realistic with the success of generative adversarial networks (GANs), making face recognition (FR) systems suffer from unprecedented security concerns. In this paper, we investigate and introduce a new type of adversarial attack to evade FR systems by manipulating facial content, called adversarial morphing attack (a.k.a. Amora). In contrast to adversarial noise attack that perturbs pixel intensity values by adding human-imperceptible noise, our proposed adversarial morphing attack works at the semantic level that perturbs pixels spatially in a coherent manner. To tackle the black-box attack problem, we devise a simple yet effective …
An Empirical Study On Correlation Between Coverage And Robustness For Deep Neural Networks, Yizhen Dong, Peixin Zhang, Jingyi Wang, Shuang Liu, Jun Sun, Jianye Hao, Xinyu Wang, Li Wang, Jinsong Dong, Ting Dai
An Empirical Study On Correlation Between Coverage And Robustness For Deep Neural Networks, Yizhen Dong, Peixin Zhang, Jingyi Wang, Shuang Liu, Jun Sun, Jianye Hao, Xinyu Wang, Li Wang, Jinsong Dong, Ting Dai
Research Collection School Of Computing and Information Systems
Deep neural networks (DNN) are increasingly applied in safety-critical systems, e.g., for face recognition, autonomous car control and malware detection. It is also shown that DNNs are subject to attacks such as adversarial perturbation and thus must be properly tested. Many coverage criteria for DNN since have been proposed, inspired by the success of code coverage criteria for software programs. The expectation is that if a DNN is well tested (and retrained) according to such coverage criteria, it is more likely to be robust. In this work, we conduct an empirical study to evaluate the relationship between coverage, robustness and …
Lightweight Privacy-Preserving Ensemble Classification For Face Recognition, Zhuo Ma, Yang Liu, Ximeng Liu, Jianfeng Ma, Kui Ren
Lightweight Privacy-Preserving Ensemble Classification For Face Recognition, Zhuo Ma, Yang Liu, Ximeng Liu, Jianfeng Ma, Kui Ren
Research Collection School Of Computing and Information Systems
The development of machine learning technology and visual sensors is promoting the wider applications of face recognition into our daily life. However, if the face features in the servers are abused by the adversary, our privacy and wealth can be faced with great threat. Many security experts have pointed out that, by 3-D-printing technology, the adversary can utilize the leaked face feature data to masquerade others and break the E-bank accounts. Therefore, in this paper, we propose a lightweight privacy-preserving adaptive boosting (AdaBoost) classification framework for face recognition (POR) based on the additive secret sharing and edge computing. First, we …
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 …
Multi-View Ear Recognition Based On Moving Least Square Pose Interpolation, Heng Liu, David Zhang, Zhiyuan Zhang
Multi-View Ear Recognition Based On Moving Least Square Pose Interpolation, Heng Liu, David Zhang, Zhiyuan Zhang
Research Collection School Of Computing and Information Systems
Based on moving least square, a multi-view ear pose interpolation and corresponding recognition approach is proposed. This work firstly analyzes the shape characteristics of actual trace caused by ear pose varying in feature space. Then according to training samples pose projection, we manage to recover the complete multi-view ear pose manifold by using moving least square pose interpolation. The constructed multi-view ear pose manifolds can be easily utilized to recognize ear images captured under different views based on finding the minimal projection distance to the manifolds. The experimental results and some comparisons show the new method is superior to manifold …
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 …