Deep Learning Pet Identification Using Face and Body

Abstract

According to the American Humane Association, millions of cats and dogs are lost yearly. Only a few thousand of them are found and returned home. In this work, we use deep learning to help expedite the procedure of finding lost cats and dogs, for which a new dataset is collected. We applied transfer learning methods on different convolutional neural networks for species classification and animal identification. The framework consists of seven sequential layers: data preprocessing, species classification, face and body detection with landmark detection techniques, face alignment, identification, animal soft biometrics, and recommendation. We achieved an accuracy of 98.18% on species classification. In the face identification layer, 80% accuracy was achieved. Body identification resulted in 81% accuracy. When using body identification in addition to face identification, the accuracy increased to 86.5%, with a 100% chance that the animal would be in our top 10 recommendations of matching. By incorporating animals’ soft biometric information, the system can identify animals with 92% confidence.