Multimedia Tools and Applications

, Volume 76, Issue 14, pp 15325–15340 | Cite as

Where is my puppy? Retrieving lost dogs by facial features

  • Thierry Pinheiro Moreira
  • Mauricio Lisboa Perez
  • Rafael de Oliveira Werneck
  • Eduardo Valle


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.


Face recognition Dog recognition Deep learning Convolutional networks 



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.


  1. 1.
    Ahonen T, Hadid A, Pietikinen M (2004) Face recognition with local binary patterns. In: Pajdla T, Matas J (eds) European conference on computer vision, lecture notes in computer science. doi: 10.1007/978-3-540-24670-1_36, vol 3021. Springer, Berlin Heidelberg, pp 469–481
  2. 2.
    Ahonen T, Hadid A, Pietikäinen M (2006) Face description with local binary patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intell 28:2037–2041. doi: 10.1109/TPAMI.2006.244 CrossRefzbMATHGoogle Scholar
  3. 3.
    Belhumeur P, Hespanha J, Kriegman D (1997) Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720. doi: 10.1109/34.598228 CrossRefGoogle Scholar
  4. 4.
    Belhumeur P N, Jacobs D W, Kriegman D J, Kumar N (2013) Localizing parts of faces using a consensus of exemplars. In: IEEE transactions on pattern analysis and machine intelligence, vol 35, pp 2930– 2940Google Scholar
  5. 5.
    Bergstra J, Yamins D, Cox D (2013) Making a science of model search: hyperparameter optimization in hundreds of dimensions for vision architectures. In: International conference on machine learning., pp 115–123
  6. 6.
    Bradski G (2000) The OpenCV library. Dr Dobb’s Journal of Software ToolsGoogle Scholar
  7. 7.
    Chiachia G, Falcão AX, Pinto N, Rocha A, Cox D (2014) Learning person-specific representations from faces in the wild. IEEE Trans Inf Forensics Secur 9(12):2089–2099. doi: 10.1109/TIFS.2014.2359543.
  8. 8.
    Cox D, Pinto N (2011) Beyond simple features: a large-scale feature search approach to unconstrained face recognition. In: IEEE International conference on automatic face and gesture recognition and workshops. doi: 10.1109/FG.2011.5771385, pp 8–15
  9. 9.
    Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: IEEE conference on computer vision and pattern recognition. doi: 10.1109/CVPR.2005.177, vol 1, pp 886–893
  10. 10.
    Diamond R, Carey S (1986) Why faces are and are not special: an effect of expertise. J Exp Psychol Gen 115(2):107–117CrossRefGoogle Scholar
  11. 11.
    Felzenszwalb P, Girshick R, McAllester D, Ramanan D (2010) Object detection with discriminatively trained part-based models. IEEE Trans Pattern Anal Mach Intell 32(9):1627–1645. doi: 10.1109/TPAMI.2009.167 CrossRefGoogle Scholar
  12. 12.
    Fisher R A (1936) The use of multiple measurements in taxonomic problems. Ann Eugenics 7(2):179–188. doi: 10.1111/j.1469-1809.1936.tb02137.x CrossRefGoogle Scholar
  13. 13.
    Hinton G E, Srivastava N, Krizhevsky A, Sutskever I, Salakhutdinov R R (2012) Improving neural networks by preventing co-adaptation of feature detectors. arXiv:1207.0580
  14. 14.
    Krizhevsky A, Sutskever I, Hinton G E (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Proces Syst:1–9Google Scholar
  15. 15.
    Liu J, Kanazawa A, Jacobs D, Belhumeur P (2012) Dog breed classification using part localization. In: European conference on computer vision. Springer, pp 172–185Google Scholar
  16. 16.
    Lowe D (1999) Object recognition from local scale-invariant features. In: International conference on computer vision. doi: 10.1109/ICCV.1999.790410, vol 2, pp 1150–1157
  17. 17.
    Parkhi OM, Vedaldi A, Zisserman A, Jawahar C (2012) Cats and dogs. In: IEEE conference on computer vision and pattern recognition, pp 3498–3505Google Scholar
  18. 18.
    Pinto N, Stone Z, Zickler T, Cox D (2011) Scaling up biologically-inspired computer vision: a case study in unconstrained face recognition on facebook. In: IEEE computer society conference on computer vision and pattern recognition workshops. doi: 10.1109/CVPRW.2011.5981788, pp 35–42
  19. 19.
    Scapinello K, Yarmey A (1970) The role of familiarity and orientation in immediate and delayed recognition of pictorial stimuli. Psychon Sci 21(6):329–330. doi: 10.3758/BF03335807 CrossRefGoogle Scholar
  20. 20.
    Sermanet P, Eigen D, Zhang X, Mathieu M, Fergus R, LeCun Y (2014) Overfeat: integrated recognition, localization and detection using convolutional networks. In: International conference on learning representations, CBLS.
  21. 21.
    Sirovich L, Kirby M (1987) Low-dimensional procedure for the characterization of human faces. J Opt Soc Am A 4(3):519–524. doi: 10.1364/JOSAA.4.000519. CrossRefGoogle Scholar
  22. 22.
    Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: IEEE Conference on Computer Vision and Pattern RecognitionGoogle Scholar
  23. 23.
    Turk M, Pentland A (1991) Face recognition using eigenfaces. In: IEEE conference on computer vision and pattern recognition. doi: 10.1109/CVPR.1991.139758, pp 586–591
  24. 24.
    Wang X, Ly V, Sorensen S, Kambhamettu C (2014) Dog breed classification via landmarks. In: IEEE international conference on image processing, IEEE, pp 5237–5241Google Scholar
  25. 25.
    Wiskott L, Fellous J M, Krüger N, Von Malsburg C D (1997) Face recognition by elastic bunch graph matching. IEEE Trans Pattern Anal Mach Intell 19:775–779. doi: 10.1109/34.598235 CrossRefGoogle Scholar
  26. 26.
    Xu Y, Zhang D, Yang J, Yang J Y (2011) A two-phase test sample sparse representation method for use with face recognition. Circuits and Systems for Video Technology 21(9):1255–1262. doi: 10.1109/TCSVT.2011.2138790 MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Thierry Pinheiro Moreira
    • 1
  • Mauricio Lisboa Perez
    • 2
  • Rafael de Oliveira Werneck
    • 2
  • Eduardo Valle
    • 2
    • 3
  1. 1.LIV Laboratory, Institute of Computing (IC)University of Campinas (Unicamp)CampinasBrazil
  2. 2.RECOD Laboratory, Institute of Computing (IC)University of Campinas (Unicamp)CampinasBrazil
  3. 3.Department of Computer Engineering and Industrial Automation (DCA), School of Electrical and Computer Engineering (FEEC)University of Campinas (Unicamp)CampinasBrazil

Personalised recommendations