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Where is my puppy? Retrieving lost dogs by facial features

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Abstract

A pet that goes missing is among many people’s worst fears: a moment of distraction is enough for a dog or a cat wandering off from home. Some measures help matching lost animals to their owners; but automated visual recognition is one that — although convenient, highly available, and low-cost — is surprisingly overlooked. In this paper, we inaugurate that promising avenue by pursuing face recognition for dogs. We contrast four ready-to-use human facial recognizers (EigenFaces, FisherFaces, LBPH, and a Sparse method) to two original solutions based upon convolutional neural networks: BARK (inspired in architecture-optimized networks employed for human facial recognition) and WOOF (based upon off-the-shelf OverFeat features). Human facial recognizers perform poorly for dogs (up to 60.5 % accuracy), showing that dog facial recognition is not a trivial extension of human facial recognition. The convolutional network solutions work much better, with BARK attaining up to 81.1 % accuracy, and WOOF, 89.4 %. The tests were conducted in two datasets: Flickr-dog, with 42 dogs of two breeds (pugs and huskies); and Snoopybook, with 18 mongrel dogs.

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Notes

  1. http://www.humanesociety.org/issues/pet_overpopulation/facts/pet_ownership_statistics.html(December 2015).

  2. https://www.aspca.org/about-us/press-releases/how-many-pets-are-lost-how-many-find-their-way-home-aspca-survey-has-answers https://www.aspca.org/about-us/press-releases/how-many-pets-are-lost-how-many-find-their-way-home-aspca-survey-has-answers(March 2015).

  3. For example, in August 2015, the New England Aquarium and MathWorks have launched a large competition for identifying individual endangered whales: https://www.kaggle.com/c/noaa-right-whale-recognition.

  4. http://www.robots.ox.ac.uk/~vgg/data/pets/

  5. http://cilvr.nyu.edu/doku.php?id=software:overfeat:start (August 2015).

  6. Available at:http://www.recod.ic.unicamp.br/~rwerneck/datasets/flickr-dog/.

  7. www.github.com/giovanichiachia/simple-hp.

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Acknowledgments

Special thanks to the veterinary doctor Marjorie de Oliveira Franco, along with the Zoonoses Control Center of São José dos Campos - SP, for producing the Snoopybook dataset, and providing it for research. And thanks to Giovani Chiachia for its help on using simple-hp, tips on how to perform the experiments and helping organizing the dataset.

Author information

Authors and Affiliations

  1. LIV Laboratory, Institute of Computing (IC), University of Campinas (Unicamp), Campinas, Brazil

    Thierry Pinheiro Moreira

  2. RECOD Laboratory, Institute of Computing (IC), University of Campinas (Unicamp), Campinas, Brazil

    Mauricio Lisboa Perez, Rafael de Oliveira Werneck & Eduardo Valle

  3. Department of Computer Engineering and Industrial Automation (DCA), School of Electrical and Computer Engineering (FEEC), University of Campinas (Unicamp), Av. Albert Einstein, 400, Campinas, 13083-852, SP, Brazil

    Eduardo Valle

Authors
  1. Thierry Pinheiro Moreira
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  2. Mauricio Lisboa Perez
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  3. Rafael de Oliveira Werneck
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  4. Eduardo Valle
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Corresponding author

Correspondence to Thierry Pinheiro Moreira.

Additional information

T. Moreira, M. Perez, and R. Werneck contributed equally to this work.

This work was supported in part by FAPESP, CAPES, CNPq, and SAMSUNG.

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Moreira, T.P., Perez, M.L., Werneck, R. et al. Where is my puppy? Retrieving lost dogs by facial features. Multimed Tools Appl 76, 15325–15340 (2017). https://doi.org/10.1007/s11042-016-3824-1

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