# Where Is My Puppy? Retrieving Lost Dogs by Facial Features

arXiv: https://arxiv.org/abs/1510.02781 · HTML: https://mlsanity.com/paper/1510.02781/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2015.

## Built on by (most cited first, 3 of 3)

- [PetFace: A Large-Scale Dataset and Benchmark for Animal Identification](https://mlsanity.com/paper/2407.13555/) (2024)
- [WildFusion: Individual Animal Identification with Calibrated Similarity Fusion](https://mlsanity.com/paper/2408.12934/) (2024)
- [CzechLynx: A Dataset for Individual Identification and Pose Estimation of the Eurasian Lynx](https://mlsanity.com/paper/2506.04931/) (2025)

## Abstract (arXiv)

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.