iFBI: Lightweight Breed and Individual Recognition for Cats and Dogs

Abstract

As the pet industry develops, fine-grained breed recognition and individual recognition have emerged as essential components in biometric measurement systems for intelligent pet monitoring, aiming to identify the specific breed of a pet in an image and to recognize the same individual across multiple images. These capabilities lay the foundation for downstream tasks such as posture estimation and emotion analysis, supporting a wide range of real-world applications. Despite the substantial advancements achieved in existing research, two critical issues remain to be solved: the diversity of object poses affects representation in complex scenarios, and the conflict between model complexity and performance hinders application in resource-constrained conditions. To address the above issues, we propose integrated face and body information (iFBI) for a lightweight breed and individual recognition scheme that integrates multiple pose information by a lightweight model. Specifically, a face alignment (FA) module and a body posture-guided (BPG) module are proposed to separate face and body information from the input images, fully capturing the posture details while suppressing background areas. To further maximize the discrimination between individual samples, we design a multilevel representation recognition (MRR) module that dynamically integrates complementary semantic features of face and body, consequently generating more discriminative features. In addition, a dynamic convolutional model compression (DCMC) method is implemented with an improved dual-branch backbone that significantly reduces computational costs while enhancing model performance. Extensive experiments on two self-built datasets—pet with fine-grained breed (Pet-FB) dataset and pet with diverse posture (Pet-DP) dataset—and four public datasets indicate that iFBI yields superior performance in both fine-grained breed recognition and individual recognition tasks. The source code and self-built datasets—Pet-FB and Pet-DP—are available at our GitHub repository.