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Valuation of facial image likeness under different posture

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Abstract

Image similarity search is crucial in various applications. Determining similarities among objects of images is a key point of perceptual image resemblance. Identification of varied pose of human face is the biggest issue under image resemblance. Mostly the previous research uses different distance metric algorithms to measure the image resemblance where the distance between two objects of images is used to determine the degree of resemblance. This paper presented a streamlined algorithm termed as Description Proximity Cover for computing the perceptual image resemblance based on neighborhoods. Comparative analysis of DPC has done with other existing metric based algorithms using large dataset of images. Further the resemblance of facial expression of images has also carried out using DPC. It has been found from the experimental results that retrieval performance may be significantly improved using Description Proximity Cover.

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References

  • Celebi ME, Aslandogan YA (2005) Human perception-driven, similarity-based access to image databases. In: FLAIRS conference, pp 245–250

  • Chiranjeevi P, Gopalakrishnan V, Moogi P (2015) Neutral face classification using personalized appearance models for fast and robust emotion detection. IEEE Trans Image Process 24(9):2701–2711

    Article  MathSciNet  Google Scholar 

  • Fei L et al (2016) Double-orientation code and nonlinear matching scheme for palmprint recognition”. Pattern Recognit 49:89–101

    Article  Google Scholar 

  • Huttenlocher DP, Klanderman G, Rucklidge WJ (1993) Comparing images using the Hausdorff distance. IEEE Trans Pattern Anal Mach Intell 15(9):850–863

    Article  Google Scholar 

  • IITK Database (2002). http://www.cse.iitk.ac.in/users/vision/Image%20Dataset/Human%20Face/. Accessed June 2014

  • Kamachi M, Lyons M, Gyoba J (1998) The japanese female facial expression (jaffe) database. http://www.kasrl.org/jaffe

  • Landre J, Truchetet F (2007) Image retrieval with binary hamming distance. VISAPP (2) 7:237–240

    Google Scholar 

  • Norouzi M, Fleet DJ, Salakhutdinov RR (2012) Hamming distance metric learning. In: Advances in neural information processing systems, pp 1061–1069

  • Nutanong S, Jacox EH, Samet H (2011) An incremental Hausdorff distance calculation algorithm. InL Proceedings of the VLDB endowment 4(8):506–517

    Article  Google Scholar 

  • Patnaik KS, Sanober A (2012) Plastic surgery facial image correspondence: a near set approach. Int J Comput Appl 42(1):16–22

    Google Scholar 

  • Rogowitz BE et al (1998) Perceptual image similarity experiments. Human vision and electronic imaging. In: Proceedings of the SPIE, vol 3299

  • Sajwan M, Patnaik KS (2017) Quantifiable image nearness approach using descriptive neighbourhood. In: Proceedings of the international conference on signal, networks, computing, and systems, pp 73–84

    Google Scholar 

  • Tang M, Yu Y, Aref WG, Malluhi QM, Ouzzani M (2015) Efficient processing of hamming-distance-based similarity-search queries over MapReduce. In: EDBT, pp 361–372

  • Veltkamp RC (2001) Shape matching: similarity measures and algorithms. In: Shape modeling and applications. SMI international conference, IEEE, pp 188–197

  • Wang HH, Mohamad D, Ismail NA (2010) Approaches, challenges and future direction of image retrieval, vol 2. arXiv preprint arXiv:1006.4568

  • Zhang X, et al (2013) Hmsearch: an efficient hamming distance query processing algorithm. In: Proceedings of the 25th international conference on scientific and statistical database management, ACM

  • Zhang D et al (2018) Efficient and accurate Hausdorff distance computation based on diffusion search”. IEEE Access 6:1350–1361

    Article  Google Scholar 

Download references

Acknowledgements

This research is relatively supported by the database of human faces provided in Database (2002), Japanese Female Facial Expression (JAFFE) Database (1998).

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Birla Institute of Technology Mesra, Ranchi, India

    Priyanka Srivastava & K. S. Patnaik

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  1. Priyanka Srivastava
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  2. K. S. Patnaik
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Correspondence to Priyanka Srivastava.

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Srivastava, P., Patnaik, K.S. Valuation of facial image likeness under different posture. Microsyst Technol 25, 4625–4635 (2019). https://doi.org/10.1007/s00542-019-04441-z

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  • DOI: https://doi.org/10.1007/s00542-019-04441-z

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