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Person Re-identification by Integrating Static Texture and Shape Cues 12th

Person Re-identification by Integrating Static Texture and S...

引用

12th Chinese Conference on Biometric recognition, CCBR 2017

作者： Madongo, Canaan Tinotenda Huang, Di Chen, Jiaxin Laboratory of Intelligent Recognition and Image Processing School of Computer Science and Engineering Beihang University Beijing100191 China Department of Electrical and Computer Engineering New York University Abu Dhabi Abu Dhabi United Arab Emirates

ISBN: (纸本)9783319699226

Person Re-Identification (Re-ID) is a challenging task with wide ranging applications in various fields. This paper presents a novel hand-crafted method for this issue, enhancing the state of the art ones in literature in two aspects. In contrast to most current studies that analyze texture features, we propose a discriminative and compact shape feature by applying Procrustes shape analysis. It not only retains shape distinctiveness of an individual sample, but also alleviates cross-view impacts. Furthermore, we combine the shape feature with some current popular texture features, namely LOMO and mid-level filters, so that the advantages of multiple clues can be jointly used. A score level fusion strategy is finally adopted to optimally integrate their credits. Evaluated on two public benchmarks, i.e. VIPeR and CUHK03, the proposed method achieves very competitive results, indicating its effectiveness in person Re-ID. © 2017, Springer International Publishing AG.

关键词： Computers

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Benchmarking asymmetric 3D-2D face recognition systems

Benchmarking asymmetric 3D-2D face recognition systems

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2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture recognition, FG 2013

作者： Zhao, Xi Zhang, Wuming Evangelopoulos, Georgios Huang, Di Shah, Shishir K. Wang, Yunhong Kakadiaris, Ioannis A. Chen, Liming Computational Biomedicine Lab Department of Computer Science University of Houston Houston TX 77204-3010 United States LIRIS Lab Ecole Centrale de Lyon 69134 cedex France Laboratory of Intelligent Recognition and Image Processing School of Computer Science and Engineering Beihang University Beijing China

ISBN: (纸本)9781467355452

Asymmetric 3D-2D face recognition (FR) aims to recognize individuals from 2D face images using textured 3D face models in the gallery (or vice versa). This new FR scenario has the potential to be readily deployable in field applications while still keeping the advantages of 3D FR solutions of being more robust to pose and lighting variations. In this paper, we propose a new experimental protocol based on the UHDB11 dataset for benchmarking 3D-2D FR algorithms. This new experimental protocol allows for the study of the performance of a 3D-2D FR solution under pose and/or lighting variations. Furthermore, we also benchmarked two state of the art 3D-2D FR algorithms. One is based on the Annotated Deformable Model (using manually labeled landmarks in this paper) using manually labeled landmarks whereas the other makes use of Oriented Gradient Maps along with an automatic pose estimation through random forest. © 2013 IEEE.

关键词： Face recognition

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Template selection for on-line signature verification

Template selection for on-line signature verification

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International Conference on Pattern recognition

作者： Ningning Liu Yunhong Wang Intelligent Recognition and Image Processing Laboratory School of Computer Science and Engineering BeiHang University Beijing China

ISBN: (纸本)9781424421749

In this paper, we propose two effective methods to perform automatic template selection where the goal is to select prototype signature templates for a user from a given set of online signatures. The first method employs a clustering strategy to choose a template set that best represents the intra-class variations, while the second method selects templates that exhibit maximum similarity with the rest of the signatures. In the experiment, two typical online signature verification have been employed, respectively based on global and local features, and the verifying results on a database Task2 of SVC2004 (First Signature Verification Competition 2004), with 20 genuine signatures and 20 skilled forgeries for each set, indicate that two proposed selection procedures as presented here results in better performance than random template selection.

关键词： Handwriting recognition Prototypes Shape measurement Writing image recognition image processing laboratories Computer science Design engineering Spatial databases

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Expression-Latent-Space-guided GAN for Facial Expression Animation based on Discrete labels

Expression-Latent-Space-guided GAN for Facial Expression Ani...

引用

International Conference on Automatic Face and Gesture recognition

作者： Xueping Wang Weixin Li Di Huang Intelligent Recognition and Image Processing Laboratory School of Computer Science and Engineering Beihang University Beijing China

ISBN: (纸本)9781665431774

Facial expression animation aims to synthesize face images that correspond to the target expression in a continuum. This is a challenging task because the animation not only cares about the smooth transition in the generated sequence but also needs to take the facial expression and identity details into consideration. Most existing expression animation methods resort to continuous expression labels, e.g. Action Units (AUs) or landmark sequences. Compared with discrete expression labels, the annotations of a considerable part of them are ambiguous and prone to errors. However, how to animate facial expression conditioned on discrete expression labels is less investigated and existing methods cannot generate satisfactory facial details. To tackle these problems, we propose an end-to-end Expression-Latent-Space-guided Generative Adversarial Network (ELS-GAN) model, which utilizes discrete expression labels as input to generate images with expected expressions, and employs expression latent space learning to control the expression changing process. An expression ranking loss is also proposed to strengthen expression intensity learning during generation. Moreover, we put forward a Self-Attention Generator to synthesize face images with fine details by considering both local areas and the long-range dependency of different areas. Extensive experiments show that our method can generate continuous intermediate expression between source and target expressions only conditioned on discrete labels and superior results are achieved compared with state-of-the-art methods.

关键词： Interpolation Face recognition Genetic expression Process control Gesture recognition Animation Generative adversarial networks

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Gender recognition based on fusion on face and gait information

Gender recognition based on fusion on face and gait informat...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： De Zhang Yun-Hong Wang Intelligent Recognition and Image Processing Laboratory School of Computer Science and Engineering Beihang University Beijing China

This paper considers the combination of face and gait biometrics from the same walking sequence to carry out gender recognition. A camera is capturing the side view of a person, while another camera is placed to record the face of the same person at the front view. After these videos are acquired, we extract the silhouette images from the gait videos and normalized frame images decomposed from the face videos. Then, for face classification, we introduce PCA to reduce the image dimension and SVM to classify gender, for gait classification, we divide the silhouette into seven parts and extract features from each and also employ SVM to classify gender. On the decision level, the sum rule is applied to implement the fusion of these two classification results. The final fusion results show an improvement on correct classification rate.

关键词： Face Support vector machines Cameras Principal component analysis Feature extraction Face recognition Databases

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On-line signature verification based on fusion of global and local information

On-line signature verification based on fusion of global and...

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International Conference on Wavelet Analysis and Pattern recognition (ICWAPR)

作者： Lei Hu Yun-Hong Wang Intelligent Recognition and Image Processing Laboratory School of Computer Science and Engineering Beihang University Beijing China

Automatic signature verification has been an intense research area because of the social and legal acceptance and widespread use of written signatures. It is still a challenging issue because of "small sample size " problem as well as large intra-class variations and, when considering forgeries, small inter-class variations. In order to solve these problems, we propose a two-stage fusion method to get high accuracy. At first, an EDTW (enhanced dynamic time warping) algorithm and a normalized feature measure are proposed to build a classifier based on local features. The former enhances the separability between genuine and forgery signatures, while the latter approaches the problem as a two-class pattern recognition problem, which make it possible to use training signatures as many as possible. However, local method is time and resource consuming, so we then design another classifier based on global features using majority voting rule. We fuse the global and local method by two-sage serial strategy to build an on-line signature verification system. Experimental results on SVC2004 TASK2 show good performance of our system.

关键词： Handwriting recognition Pattern recognition Forgery Voting Wavelet analysis Pattern analysis Law Legal factors Fuses

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Robust Mobile Spamming Detection via Graph Patterns

Robust Mobile Spamming Detection via Graph Patterns

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International Conference on Pattern recognition

作者： Yuhang Zhao Zhaoxiang Zhang Yunhong Wang Jianyun Liu Intelligent Recognition and Image Processing Lab Beijing Key Laboratory of Digital Media School of Computer Science and Engineering Beihang University

ISBN: (纸本)9781467322164

Short message service (SMS) is now an indispensable way of social communication. However the mobile spam is getting increasingly serious, troubling users' daily life and ruining the service quality. We propose a novel approach for spam message detection based on mining the underlying social network of SMS activities. Comparing with strategies on keywords or flow detection, our network-based approach is more robust and difficult to defeat by human spammers. Various levels of features are employed to describe multiple aspects of the network, such as static structures, node activities and evolving situations. Experimental results on real dataset illustrate effectiveness of various features, showing our promising results.

关键词： data mining electronic messaging feature extraction mobile computing network theory (graphs) social networking (online) unsolicited e-mail

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Moving Object Detection in Aerial Video

Moving Object Detection in Aerial Video

引用

International Conference on Machine Learning and Applications (ICMLA)

作者： Yunfei Wang Zhaoxiang Zhang Yunhong Wang Laboratory of Intelligent Recognition and Image Processing School of Computer Science and Engineering Beihang University Beijing China

We address the problem of moving object detection in aerial video. Moving object detection in aerial video is still a challenging problem for the reason that when capturing the video the camera (or the platform) is moving all the time. As a result, the problem is detecting moving object from moving background which is much more difficult than the case that the background is constant. To this end, a novel approach is proposed in this paper. Moving object detection in stationary scene usually modeling the pixel value changes over time, but in aerial video the change does not have regular patterns. Therefore, we model the motion of the background rather than modeling the background directly. The optical flow between every two adjacent frames is computed first to get the motion information for each pixel. Based on this, we define a notion named ``pixel motion process" which means the motion changes (the optical flow value changes) of a particular pixel over time, and transfer the Gaussian mixture model framework used for modeling background in the stationary scene to model the background motion. The result is an accurate, adaptive and general background motion model which is used to detect foreground moving objects. Experimental results demonstrate the effectiveness of our approach.

关键词： Adaptation models Cameras Mathematical model Computational modeling Computer vision Object detection Optical imaging

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Ethnicity classification based on fusion of face and gait

Ethnicity classification based on fusion of face and gait

引用

IAPR International Conference on Biometrics (ICB)

作者： De Zhang Yunhong Wang Zhaoxiang Zhang Maodi Hu Intelligent Recognition and Image Processing Laboratory School of Computer Science and Engineering Beihang University Beijing China

The recognition of ethnicity of an individual can be very useful in a video-based surveillance system. In this paper, we propose a multimodal biometric system involving an integration of frontal face and lateral gait, for the specific problem of ethnicity classification. This system performs a feature fusion to improve the discrimination of human ethnicity. Face features are extracted by means of the uniform LBP operator and gait information is characterized by a spatio-temporal representation. Afterwards, canonical correlation analysis (CCA), as a powerful tool to relate two sets of measurements, is used to fuse the two modalities at the feature level. A database including 36 walking people from East Asia and South America is built for the purpose of ethnicity classification. The experimental results show that the ethnicity recognition rate is improved by fusing face and gait information.

关键词： Face Feature extraction Databases Support vector machines Cameras Vectors Legged locomotion

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Carrying object detection and tracking based on body main axis

Carrying object detection and tracking based on body main ax...

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International Conference on Wavelet Analysis and Pattern recognition (ICWAPR)

作者： Yue Qi Guo-Chang Huang Yun-Hong Wang Intelligent Recognition and Image Processing Laboratory School of Computer Science and Engineering Beihang University Beijing China

In this paper, we propose a method to examine whether a walking person is carrying objects using the width of human body contour. First we concentrate on the detection of the moving people contour and then human contour width is projected to the feature space for training a classifier. We introduce principal component analysis for dimension reduction and support vector machine to classify the carrying object status. If the status is true, the carrying object region is figured out by analyzing human contour shape. We utilize the outmost vertex from human contour to track the carrying object such as a rucksack or luggage. The experimental results demonstrate that our approach is encouraging.

关键词： Object detection Humans Principal component analysis Support vector machines Feature extraction Video sequences Pattern recognition Legged locomotion Filtering Wavelet analysis

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