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Face synthesis from near-infrared to visual light via sparse representation

Face synthesis from near-infrared to visual light via sparse...

引用

IEEE International Joint Conference on Biometrics (IJCB)

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

This paper presents a novel method for synthesizing artificial visual light (VIS) face images from near-infrared (NIR) inputs. Active NIR imaging is now widely employed because it is unobtrusive, invariant of environmental illuminations, and can penetrate glasses and sweats. Unfortunately, NIR imaging exhibits discrepant photic properties compared with VIS imaging. Based on recent results of re search on compressive sensing, natural images can be compressed and recovered with an overcomplete dictionary by sparse representation coefficients. In our approach a pair wise dictionary is trained from randomly sampled coupled face patches, which contains sparse coded base functions to reconstruct representation coefficients via l 1 -minimization. We will demonstrate that this method is robust to moderate pose and expression variations, and is efficient in computing. Comparative experiments are conducted with state-of the-art algol 1 -minimization. We will demonstraterithms.

关键词： Training Principal component analysis Encoding Educational institutions Accuracy

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Hierarchical and discriminative bag of features for face profile and ear based gender classification

Hierarchical and discriminative bag of features for face pro...

引用

IEEE International Joint Conference on Biometrics (IJCB)

作者： Guangpeng Zhang Yunhong Wang Laboratory of Intelligent Recognition and Image Processing Beijing Key Laboratory of Digital Media School of Computer Science and Engineering Beihang University Beijing China

ISBN: (纸本)9781457713583

Gender is an important demographic attribute of human beings, automatic face based gender classification has promising applications in various fields. Previous methods mainly deal with frontal face images, which in many cases can not be easily obtained. In contrast, we concentrate on gender classification based on face profiles and ear images in this paper. Hierarchical and discriminative bag of features technique is proposed to extract powerful features which are classified by support vector classification (SVC) with histogram intersection kernel. With the output of SVC, fusion of multi-modalities is performed at the score level based on Bayesian analysis to improve the accuracy. Experiments are conducted using texture images of the UND biometrics data sets Collection F, and average classification accuracy of 97.65% is achieved, which is comparable to the state of the art. Our work can be used in cooperate with existing frontal face based methods for accurate multi- view gender classification.

关键词： Educational institutions

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image processing On-Board Small Remote Sensing Satellites

Image Processing On-Board Small Remote Sensing Satellites

引用

2010 4th International Conference on intelligent Information Technology Application (IITA2010)

作者： Roberto Arenas Lara Lab of Intelligent Recognition and Image Processing Beihang University

Current Remote Sensing space missions have very restricted image processing capabilities on-board,and also the improvement of the down-link on Earth Observation Satellites is a topic on the table,mostly because of the amount of data generated by the sensors,the short time to download of the images from the satellites and the limited bandwidth *** overview of recent remote sensing missions and the motivations that must guide these studies are presented in this paper.

关键词： image processing remote sensing on-board small satellites

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Ethnicity classification based on gait using multi-view fusion

Ethnicity classification based on gait using multi-view fusi...

引用

2010 IEEE Computer Society Conference on Computer Vision and Pattern recognition - Workshops, CVPRW 2010

作者： Zhang, De Wang, Yunhong Bhanu, Bir Intelligent Recognition and Image Processing Laboratory Beihang University Beijing 100191 China Center for Research in Intelligent Systems University of California Riverside CA 92521 United States

ISBN: (纸本)9781424470297

The determination of ethnicity of an individual, as a soft biometrics, can be very useful in a video-based surveillance system. Currently, face is commonly used to determine the ethnicity of a person. Up to now, gait has been used for individual recognition and gender classification but not for ethnicity determination. This paper focuses on the ethnicity determination based on fusion of multi-view gait. Gait Energy image (GEI) is used to analyze the recognition power of gait for ethnicity. Feature fusion, score fusion and decision fusion from multiple views of gait are explored. For the feature fusion, GEI images and camera views are put together to render a third-order tensor (x;y;view). A multilinear principal component analysis (MPCA) is used to extract features from tensor objects which integrate all views. For the score fusion, the similarity scores measured from single views are combined with a weighted SUM rule. For the decision fusion, ethnicity classification is realized on each individual view first. The classification results are then combined to make the final determination with a majority vote rule. A database of 36 walking people (East Asian and South American) was acquired from 7 different camera views. The experimental results show that ethnicity can be determined from human gait in video automatically. The classification rate is improved by fusing multiple camera views and a comparison among different fusion schemes shows that the MPCA based feature fusion performs thebest. © 2010 IEEE.

关键词： Principal component analysis

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Age classification based on gait using HMM

Age classification based on gait using HMM

引用

2010 20th International Conference on Pattern recognition, ICPR 2010

作者： Zhang, De Wang, Yunhong Bhanu, Bir Intelligent Recognition and Image Processing Laboratory School of Computer Science and Engineering Beihang University Beijing 100191 China Center for Research in Intelligent Systems University of California Riverside CA 92521 United States

ISBN: (纸本)9780769541099

In this paper we propose a new framework for age classification based on human gait using Hidden Markov Model (HMM). A gait database including young people and elderly people is built. To extract appropriate gait features, we consider a contour related method in terms of shape variations during human walking. Then the image feature is transformed to a lower-dimensional space by using the Frame to Exemplar (FED) distance. A HMM is trained on the FED vector sequences. Thus, the framework provides flexibility in the selection of gait feature representation. In addition, the framework is robust for classification due to the statistical nature of HMM. The experimental results show that video-based automatic age classification from human gait is feasible and reliable. © 2010 IEEE.

关键词： Hidden Markov models

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Lossless Compression On-Board Remote Sensing Satellites

Lossless Compression On-Board Remote Sensing Satellites

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2010 International Conference on Computer Science and Sports Engineering (CSSE 2010)

作者： Roberto Arenas Lara Lab of Intelligent Recognition and Image Processing Beihang University Beijing P.R. China

image processing on-board satellites is no longer a nice to have feature. Lossless image compression allows increasing the stored data on-board satellite and transmitting it to the ground station without losing quality of the images. An overview of development trend in this area, as well as brief state-of-the-art lossless image compression methods on-board satellites are presented in this paper.

关键词： Lossless compression image processing on-board satellite remote sensing

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Ethnicity classification based on gait using multi-view fusion

Ethnicity classification based on gait using multi-view fusi...

引用

IEEE Computer Society Conference on Computer Vision and Pattern recognition Workshops (CVPRW)

作者： De Zhang Yunhong Wang Bir Bhanu Center for Research in Intelligent Systems University of California Riverside CA USA Intelligent Recognition and Image Processing Laboratory Beihang University Beijing China

The determination of ethnicity of an individual, as a soft biometrics, can be very useful in a video-based surveillance system. Currently, face is commonly used to determine the ethnicity of a person. Up to now, gait has been used for individual recognition and gender classification but not for ethnicity determination. This paper focuses on the ethnicity determination based on fusion of multi-view gait. Gait Energy image (GEI) is used to analyze the recognition power of gait for ethnicity. Feature fusion, score fusion and decision fusion from multiple views of gait are explored. For the feature fusion, GEI images and camera views are put together to render a third-order tensor (x; y; view). A multilinear principal component analysis (MPCA) is used to extract features from tensor objects which integrate all views. For the score fusion, the similarity scores measured from single views are combined with a weighted SUM rule. For the decision fusion, ethnicity classification is realized on each individual view first. The classification results are then combined to make the final determination with a majority vote rule. A database of 36 walking people (East Asian and South American) was acquired from 7 different camera views. The experimental results show that ethnicity can be determined from human gait in video automatically. The classification rate is improved by fusing multiple camera views and a comparison among different fusion schemes shows that the MPCA based feature fusion performs the best.

关键词： Cameras Tensile stress Biometrics Surveillance image analysis image recognition Rendering (computer graphics) Principal component analysis Feature extraction Voting

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Age Classification Base on Gait Using HMM

Age Classification Base on Gait Using HMM

引用

International Conference on Pattern recognition

作者： De Zhang Yunhong Wang Bir Bhanu Intelligent Recognition and Image Processing Laboratory School of Computer Science and Engineering Beihang University Beijing China Center of Research in Intelligent Systems University of California Riverside CA USA

关键词： Hidden Markov models Senior citizens Databases Legged locomotion Feature extraction Humans Pixel

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A new roadmap constitution method based on dividing grid and level-set

A new roadmap constitution method based on dividing grid and...

引用

International Symposium on Intelligence Information processing and Trusted Computing

作者： Li, Shidong Ding, Mingyue Cai, Chao Institute for Information Engineering of Hubei University for Nationalities Enshi Hubei China Institute for Pattern Recognition and Artificial Intelligence National Lab. for Multi-spectral Information Processing Technologies HUST Wuhan China College of Life Science and Technology Image Processing and Intelligent Control Key Laboratory of Education Ministry of China HUST Wuhan China

ISBN: (纸本)9780769541969

Roadmap methods were widely used in route planning fields, both for robots and unmanned aircrafts. Traditional roadmap is constituted by connecting the vertexes of convex obstacle, which is related to the locations of start and goal of route, so the roadmap can not be reused when anyone of start and goal changed. Absence of reusability reduces the efficiency of route planning by using roadmap. A new roadmap constitution method was proposed in this paper, which is build on dividing the 2D configuration space into multi-scale grids and setting nodes of roadmap on grid boundary using Level-Set method, same method was used to construct the map cells of roadmap between any two nodes lays on same grid . The roadmap constituted with new method can be reused when the start or goal was changed;it is efficient for replanning problem. © 2010 IEEE.

关键词： Reusability

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Gender recognition based on fusion of face and multi-view gait

Gender recognition based on fusion of face and multi-view ga...

引用

3rd International Conference on Advances in Biometrics, ICB 2009

作者： De, Zhang Wang, Yunhong Intelligent Recognition and Image Processing Laboratory School of Computer Science and Engineering Beihang University Beijing 100191 China

ISBN: (纸本)3642017924

In this paper, we consider the problem of gender recognition based on face and multi-view gait cues in the same walking sequence. The gait cues are derived from multiple simultaneous camera views. Meanwhile, the face cues are captured by a camera at front view. According to this setup, we build a database including 32 male subjects and 28 female subjects. Then, for face, we normalize the frame images decomposed from videos and introduce PCA to reduce image dimension. For gait, we extract silhouettes from videos and employ an improved spatio-temporal representation on the silhouettes to obtain gait features. SVM is then used to classify gender with face features and gait features from each view respectively. We employ three fusion approaches involving voting rule, weighted voting rule and Bayes combination rule at the decision level. The effectiveness of various approaches is evaluated on our database. The experimental results of integrating face and multi-view gait show an obvious improvement on the accuracy of gender recognition. © Springer-Verlag Berlin Heidelberg 2009.

关键词： Cameras

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