Face Recognition of the Rhinopithecus Roxellana QinlingensisBased on Improved HOG and Sparse Representation

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International Journal of Advanced Network, Monitoring and Controls

Xi'an Technological University

Subject: Computer Science, Software Engineering

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VOLUME 2 , ISSUE 4 (December 2017) > List of articles

Face Recognition of the Rhinopithecus Roxellana QinlingensisBased on Improved HOG and Sparse Representation

Cuan Ying / Shi Yaojie

Keywords : Rhinopithecus roxellana qinlingensis, Face recognition, Histogram of Oriented Gradient, Sparse Dictionary, Sparse Representation

Citation Information : International Journal of Advanced Network, Monitoring and Controls. Volume 2, Issue 4, Pages 194-198, DOI: https://doi.org/10.1109/iccnea.2017.42

License : (CC BY-NC-ND 4.0)

Published Online: 24-April-2018

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ABSTRACT

With the researches on face recognition of Rhinopithecusroxellanaqinlingensis, this thesis comes up with some methods that refining traditional HOG and Sparse Representation in order to improve the efficiency in recognizing golden monkeys. As we know, improved HOG is an optimal way to show partial information of an image. Besides, it can also plays an crucial role in staying stability in both optical and geometric distortion, which means the changes in expressions, postures and angles of golden monkeys can also be ignored. By using these characteristics as a alternation of original images to be a part of Sparse dictionary, and make a facial recognition on golden monkey with Sparse Representation, which can be a ideal method to erase many unnecessary messages and improve the accuracy on facial recognition of golden monkeys. Compared with mainstream method in recognition, this method is more reliable and effective and has a higher efficiency in recognition.

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REFERENCES

YongYan-ge,LiuSi-yang,ZhangYong-wen.RhinopithecusRoxellana[J].Forest & Humankind,2013(2):40-47.

 

Wang Xiao-wei,Lv Jiu-quan,Guo Song-tao et al.Foraging biology of the Foraging biology[J].Bulletin of Biology, 2006, 41(3):13-14.

 

Zhang Peng,Li Bao-guo,.Kazuo WADA et al.Social structure of a group of Sichuan snub-nosed monkeys(Rhinopithecus roxellana)in the Qinling Mountains of China[J].Current Zoology,2003, 49(6):727-735.

 

Yang S, Luo P, Loy C C, et al. From Facial Parts Responses to Face Detection: A Deep Learning Approach[C]// IEEE International Conference on Computer Vision. IEEE Computer Society, 2015:3676-3684.

 

Lu J, Liong V E, Zhou J. Simultaneous Local Binary Feature Learning and Encoding for Face Recognition[C]// IEEE International Conference on Computer Vision. 2015:3721-3729.

 

Huang Z, Wang R, Shan S, et al. Projection Metric Learning on Grassmann Manifold with Application to Video based Face Recognition[J]. 2015:140-149.

 

Klare B F, Klein B, Taborsky E, et al. Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A[J]. 2015:1931-1939.

 

Wei Dong-mei,Zhou Wei-dong.Face recognition using collaborative representation with neighbors[J].Journal of Xidian University:Science and Technology,2015,42(3):115-121.

 

Zeng Chen-ying Research of Image Monitoring and Identification Oriented to Rare Wild Animals Protection[D].Beijing Forestry University,2015.

 

Xie Su-yi.Research on Pet-Cat Face Detection Algorithm[D].Shanghai Jiaotong University,2015.

 

Dalal N, Triggs B. Triggs, B.: Histograms of Oriented Gradients for Human Detection. In: CVPR[J]. 2005, 1(12):886-893.

 

Mao Hui-yun.Feature Analysis and Machine Learning of Facial Beauty Attractiveness[D].South China University of Technology, 2011

 

Zhao Qian,Zhu Hua-wei,Zeng Zhao-hui,et al.Target Tracking Fusion Algorithm Based on YUV Color Space Characteristic[J].Video Engineering,2013,37(9): 187-191.

 

Tian Xian-xian,Bao Hong,Xu Cheng.Improved HOG Algorithm of Pedestrian Detection[J].Computer Science, 2014,41(9):320-324.

 

Cheng Jian,Li Lan,Wang Hai-xu.SAR Target Recognition under the Framework of Sparse Representation[J].Journal of University of Electronic Science and Technology of China,2014(4):524-529.

 

Zhou Jian-cheng,Zhang Wen-ting.A New Algorithm of Image Super-Resolution Reconstruction Based on MOD Dictionary-Learning.[J]Journal of Graphics,2015(3):402-406.

 

Zhang Mu-fan.Appliance of Sparse Representation based Face Recognition[D].Nanjing University of Posts and Telecommunications,2014.

 

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