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. Animal management services collect stray animals and try to find their owners, but not always successfully. Some measures may improve the chances of 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 three ready-to-use human facial recognizers (EigenFaces, FisherFaces and LBPH) to two original solutions based upon existing 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 56.1% 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.