Advancing Pet Biometric Identification: A State-of-the-Art Unified Framework for Dogs and Cats
Bibliographic record
Abstract
The growing integration of companion animals, such as dogs and cats, into households and society, alongside increasing policies and laws for pet management, necessitates reliable identification systems. Traditional methods, such as collars, microchips, and tattoos, pose issues such as invasiveness, the need for specialized scanners, and the potential for loss or error. This study introduces an AI-powered biometric identification system using smartphone images to offer a convenient and accessible alternative. We present a unified framework incorporating two advanced models: Dog Nose Network Version 2 (DNNetV2) for dog nose-print recognition and Cat Face Network (CatFaceNet) for cat facial identification. DNNetV2, the second iteration of our dog nose identification model, enhanced by the Tiny Vision Transformer and a novel MagFace-based loss function, achieves 99.8% Rank-1 identification accuracy and 99.62% verification accuracy. Similarly, CatFaceNet, leveraging human facial recognition techniques and our advancements in DNNetV2, achieves 99.96% Rank-1 identification accuracy and 99.26% verification accuracy. This comprehensive system demonstrates robust, high-performance identification for both dogs and cats, with potential applications in national pet registry, pet insurance, veterinary care, and lost pet recovery. Our unified solution establishes a new benchmark in pet biometric identification, ensuring exceptional reliability and performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".