Counting Without Numbers and Finding Without Words

TL;DR

This paper introduces a multimodal animal reunification system combining visual and acoustic biometrics, inspired by animal communication, to improve identification accuracy across species and conditions.

Contribution

It presents the first biologically inspired, species-adaptive system integrating visual and acoustic data for animal identification and reunification.

Findings

Successfully processes vocalizations from 10Hz to 4kHz.

Integrates probabilistic visual matching tolerant to appearance changes.

Demonstrates AI grounded in biological communication aids vulnerable populations.

Abstract

Every year, 10 million pets enter shelters, separated from their families. Despite desperate searches by both guardians and lost animals, 70% never reunite, not because matches do not exist, but because current systems look only at appearance, while animals recognize each other through sound. We ask, why does computer vision treat vocalizing species as silent visual objects? Drawing on five decades of cognitive science showing that animals perceive quantity approximately and communicate identity acoustically, we present the first multimodal reunification system integrating visual and acoustic biometrics. Our species-adaptive architecture processes vocalizations from 10Hz elephant rumbles to 4kHz puppy whines, paired with probabilistic visual matching that tolerates stress-induced appearance changes. This work demonstrates that AI grounded in biological communication principles can serve vulnerable populations that lack human language.