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Local Binary Pattern, Local Derivative Pattern and Skeleton Features for RGB-D Person Re-identification

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Abstract

Novel methods based on depth and skeleton information of RGB-D sensors are proposed for person re-identification. Firstly, the depth images of the body are divided into three parts, i.e., head, torso and legs. Then, each part is described using histograms of local binary pattern and local derivative pattern. Also, the local pattern descriptors are combined with Gabor features for robustness against illumination. In the next step, these features are combined with skeleton features using the score-level fusion with sum rule. The results are evaluated on the KinectREID database, and experimental results show the good performance of the proposed methods.

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References

  1. Pala F, Satta R, Fumera G, Roli F (2015) Multi-modal person Re-identification using RGB-D cameras. IEEE Trans Circuit Sys Video Tech 99:1–12

    Google Scholar 

  2. Ma B, Su Y, Jurie F (2014) Covariance descriptor based on bio-inspired features for person re-identification and face verification. Image Vis Comput 32:379–390

    Article  Google Scholar 

  3. Farenzena M, Bazzani L, Perina A, Murino V, Cristani M (2010) Person re-identification by symmetry-driven accumulation of local features In: IEEE Conference computer vision and pattern recognition, San Francisco, CA, USA, pp 2360–2367

  4. Layne R, Hospedales TM, Gong S (2012) Towards person identification and re-identification with attributes. In: ECCV, Springer, pp 402–412

  5. Wang X, Doretto G, Sebastian T, Rittscher J, Tu P (2007) Shape and appearance context modeling. In: ICCV, IEEE, pp. 1–8

  6. Ojala T, Pietikäinen M, Harwood D (1996) A comparative study of texture measures with classification based on feature distributions. Pattern Recognit 29:51–59

    Article  Google Scholar 

  7. Zhang B, Gao Y, Zhao S, Liu J (2010) Local derivative pattern versus local binary pattern: face recognition with high order local pattern descriptor. IEEE Trans Image Process 19:533–544

    Article  ADS  MathSciNet  MATH  PubMed  Google Scholar 

  8. Zhou ShR, Yin JP, Zhang JM (2013) Local binary pattern (LBP) and local phase quantization (LBQ) based on Gabor filter for face representation. Neurocomputing 116:260–264

    Article  Google Scholar 

  9. Munaro M, Fossati A, Basso A, Menegatti E, Gool LV (2014) One-shot person re-identification with a consumer depth camera. Advances in computer vision and pattern recognition. Springer, Berlin, pp 161–181

    Google Scholar 

  10. Barbosa BI, Cristani M, Bue AD, Bazzani L, Murino V (2012) Re-identification with rgb-d sensors In: 1st International workshop on re-identification, pp 433–442

  11. Zhao R, Ouyang W, and Wang X (2013) Unsupervised salience learning for person re-identification. In: IEEE conference on Computer Vision and Pattern Recognition (CVPR), pp 3586–3593

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Authors and Affiliations

  1. Semnan university, Semnan, Iran

    Zeynab Imani & Hadi Soltanizadeh

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  1. Zeynab Imani
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  2. Hadi Soltanizadeh
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Correspondence to Hadi Soltanizadeh.

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Imani, Z., Soltanizadeh, H. Local Binary Pattern, Local Derivative Pattern and Skeleton Features for RGB-D Person Re-identification. Natl. Acad. Sci. Lett. 42, 233–238 (2019). https://doi.org/10.1007/s40009-018-0736-9

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  • DOI: https://doi.org/10.1007/s40009-018-0736-9

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