Where Is My Puppy? Retrieving Lost Dogs by Facial Features
Thierry Pinheiro Moreira, Mauricio Lisboa Perez, Rafael de Oliveira, Werneck, Eduardo Valle

TL;DR
This paper explores automated facial recognition for dogs to help locate lost pets, comparing traditional and neural network methods, and demonstrating the superior performance of CNN-based solutions.
Contribution
It introduces CNN-based dog facial recognition methods, BARK and WOOF, and evaluates their effectiveness against traditional human facial recognition algorithms.
Findings
CNN methods outperform traditional algorithms significantly.
WOOF achieves up to 89.4% accuracy in dog face recognition.
Dog facial recognition is a challenging extension of human facial recognition.
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.…
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Taxonomy
MethodsConvolution · *Communicated@Fast*How Do I Communicate to Expedia? · Dropout · Dense Connections · Max Pooling · Softmax · OverFeat
