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Where is my puppy? Retrieving lost dogs by facial features


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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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.

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Correspondence to Thierry Pinheiro Moreira.

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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).

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  • Face recognition
  • Dog recognition
  • Deep learning
  • Convolutional networks