FishIR: Identifying Pufferfish Individual Based on Deep Learning and Face Recognition

Abstract

Pufferfish, globally recognized for its distinctive delicacy, carries high culinary value. However, it is also notorious for the lethal toxicity, and there is a great demand for traceability measures in the commercial trade of pufferfish to assure safety and accountability. This research introduces a novel deep learning approach, utilizing facial recognition techniques, to identify pufferfish individuals. This method specifically leverages distinctive back skin texture patterns as key biological traits. Our initial step involved assembling a collection of annotated and augmented images of Takifugu bimaculatus, a species of pufferfish native to East China Sea, which is accessible upon request. We then extensively investigated fundamental components of Deep Face Recognition (deep FR) systems, focusing on segmentation and extraction models, and assessed their effectiveness in identifying pufferfish. Following this, we developed FishIR (Fish Individual Recognition), a framework to identify pufferfish individuals that consists of four deep FR stages while incorporating enhanced segmentation and feature extraction techniques. Experimental results show that this framework successfully captures unique representations of individual pufferfish, as verified by the high accuracy achieved in recognition tasks.