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Person Identification with Pose and Identification of Known Associates

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Proceedings of 2nd International Conference on Computer Vision & Image Processing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 704))

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Abstract

This paper presents a novel application of face recognition method to identify known associates of a person and is also capable to identify a person with pose using one sample per person. The proposed method uses 3D face models. Each 3D face model is generated from a single 2D face image and contains more than eleven thousand faces and six thousand vertices. This detailed information helps to maintain the texture and shape of the face moreover increases face recognition rate. In this proposed method, unrecognized faces are stored in the secondary database and transferred from secondary to primary database if they are recognized more than the threshold value which is set experimentally. Based on this face recognition method, a new technique has been developed to create groups of people those who are seen mostly together in videos which later helps to identify the known associates of a recognized face. This system has been named as “Kore” and can be used by the security agencies to identify the known associates of a suspicious person.

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References

  1. P.N. Belhumeur, J.P. Hespanha, and D. Kriegman. Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7):711–720, 1997.

    Google Scholar 

  2. Volker Blanz and Thomas Vetter. Face recognition based on fitting a 3d morphable model. IEEE Transactions on pattern analysis and machine intelligence, 25(9):1063–1074, 2003.

    Google Scholar 

  3. Ying Cai, Meng-long Yang, and Jun Li. Multiclass classification based on a deep convolutional network for head pose estimation. Frontiers of Information Technology & Electronic Engineering, 16:930–939, 2015.

    Google Scholar 

  4. Xiujuan Chai, Shiguang Shan, Xilin Chen, and Wen Gao. Locally linear regression for pose-invariant face recognition. IEEE Transactions on Image Processing, 16(7):1716–1725, 2007.

    Google Scholar 

  5. Jongmoo Choi, Gerard Medioni, Yuping Lin, Luciano Silva, Olga Regina, Mauricio Pamplona, and Timothy C Faltemier. 3d face reconstruction using a single or multiple views. In 20th International Conference on Pattern Recognition (ICPR), 2010, pp. 3959–3962. IEEE, 2010.

    Google Scholar 

  6. A Roy Chowdhury, Rama Chellappa, Sandeep Krishnamurthy, and Tai Vo. 3d face reconstruction from video using a generic model. In IEEE International Conference on Multimedia and Expo, 2002. ICME’02. Proceedings. 2002, volume 1, pp. 449–452. IEEE, 2002.

    Google Scholar 

  7. Yepeng Guan. Robust eye detection from facial image based on multi-cue facial information. In IEEE International Conference on Control and Automation, 2007. ICCA 2007, pp. 1775–1778. IEEE, 2007.

    Google Scholar 

  8. Tal Hassner. Viewing real-world faces in 3d. In Proceedings of the IEEE International Conference on Computer Vision, pp. 3607–3614, 2013.

    Google Scholar 

  9. Tal Hassner, Liav Assif, and Lior Wolf. When standard ransac is not enough: cross-media visual matching with hypothesis relevancy. Machine Vision and Applications, 25(4):971–983, 2014.

    Google Scholar 

  10. Tal Hassner, Shai Harel, Eran Paz, and Roee Enbar. Effective face frontalization in unconstrained images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4295–4304, 2015.

    Google Scholar 

  11. Matthias Hernandez, Jongmoo Choi, and Gérard Medioni. Laser scan quality 3-d face modeling using a low-cost depth camera. In Proceedings of the 20th European Signal Processing Conference (EUSIPCO), 2012, pp. 1995–1999. IEEE, 2012.

    Google Scholar 

  12. Michael J Jones and Paul Viola. Face recognition using boosted local features. IEEE International Conference on Computer Vision, 2003.

    Google Scholar 

  13. Ira Kemelmacher-Shlizerman and Ronen Basri. 3d face reconstruction from a single image using a single reference face shape. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(2):394–405, 2011.

    Google Scholar 

  14. Burcu Kepenekci. Face recognition using gabor wavelet transform. PhD thesis, Middle East Technical University, 2001.

    Google Scholar 

  15. Aristidis Likas, Nikos Vlassis, and Jakob J Verbeek. The global k-means clustering algorithm. Pattern recognition, 36(2):451–461, 2003.

    Google Scholar 

  16. Xiao-hu Ma, Meng Yang, and Zhao Zhang. Local uncorrelated local discriminant embedding for face recognition. Frontiers of Information Technology & Electronic Engineering, 17:212–223, 2016.

    Google Scholar 

  17. G Medioni and R Waupotitsch. Face recognition and modeling in 3d. In IEEE Intl Workshop on Analysis and Modeling of Faces and Gestures (AMFG 2003), pp. 232–233, 2003.

    Google Scholar 

  18. Philip E. Miller, Allen W. Rawls, Shrinivas J. Pundlik, and Damon L. Woodard. Personal identification using periocular skin texture. In Proceedings of the 2010 ACM Symposium on Applied Computing, SAC ’10, pp. 1496–1500, New York, NY, USA, 2010. ACM.

    Google Scholar 

  19. Baback Moghaddam, Wasiuddin Wahid, and Alex Pentland. Beyond eigenfaces: Probabilistic matching for face recognition. In Third IEEE International Conference on Automatic Face and Gesture Recognition, 1998. Proceedings, pp. 30–35. IEEE, 1998.

    Google Scholar 

  20. Timo Ojala, Matti Pietikäinen, and David Harwood. A comparative study of texture measures with classification based on featured distributions. Pattern recognition, 29(1):51–59, 1996.

    Google Scholar 

  21. Xiaoyang Tan and Bill Triggs. Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE transactions on image processing, 19(6):1635–1650, 2010.

    Google Scholar 

  22. M.A. Turk and A.P. Pentland. Face recognition using eigenfaces. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1991. Proceedings CVPR ’91, pp. 586–591, Jun 1991.

    Google Scholar 

  23. Yi Yang and Deva Ramanan. Articulated pose estimation with flexible mixtures-of-parts. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011, pp. 1385–1392. IEEE, 2011.

    Google Scholar 

  24. Xiangxin Zhu and Deva Ramanan. Face detection, pose estimation, and landmark localization in the wild. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012, pp. 2879–2886. IEEE, 2012.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Thapar University, Patiala, India

    Arun Singh

Authors
  1. Arun Singh
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Correspondence to Arun Singh .

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Editors and Affiliations

  1. Computer Vision and Pattern Recognition Unit, Indian Statistical Institute, Kolkata, India

    Bidyut B. Chaudhuri

  2. School of Computing, National University of Singapore, Singapore, Singapore

    Mohan S. Kankanhalli

  3. Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Balasubramanian Raman

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Singh, A. (2018). Person Identification with Pose and Identification of Known Associates. In: Chaudhuri, B., Kankanhalli, M., Raman, B. (eds) Proceedings of 2nd International Conference on Computer Vision & Image Processing . Advances in Intelligent Systems and Computing, vol 704. Springer, Singapore. https://doi.org/10.1007/978-981-10-7898-9_6

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  • DOI: https://doi.org/10.1007/978-981-10-7898-9_6

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-7897-2

  • Online ISBN: 978-981-10-7898-9

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