Name
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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/s11042-016-3824-1 | Multimedia Tools Appl. |
Keywords | Field | DocType |
Face recognition,Dog recognition,Deep learning,Convolutional networks | Distraction,Facial recognition system,Eigenface,Pattern recognition,Computer science,Mongrel,Convolutional neural network,Speech recognition,Visual recognition,Puppy,Artificial intelligence | Journal |
Volume | Issue | ISSN |
abs/1510.02781 | 14 | 1380-7501 |
Citations | PageRank | References |
1 | 0.37 | 13 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thierry P. Moreira | 1 | 21 | 2.98 |
| Mauricio Perez | 2 | 67 | 7.64 |
| Rafael de Oliveira Werneck | 3 | 20 | 3.58 |
| Eduardo Valle | 4 | 373 | 22.17 |