Leveraging artificial intelligence for photo identification to aid CITES enforcement in combating illegal trade of the endangered humphead wrasse (Cheilinus undulatus)
IntroductionHumphead, or Napoleon, wrasse (Cheilinus undulatus) is a large reef fish highly valued in the live reef food fish trade. Overexploitation, driven primarily by demand from Chinese communities, led to its ‘Endangered’ status and CITES Appendix II listing in 2004. Hong Kong is the global im...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-03-01
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| Series: | Frontiers in Ecology and Evolution |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fevo.2025.1526661/full |
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| Summary: | IntroductionHumphead, or Napoleon, wrasse (Cheilinus undulatus) is a large reef fish highly valued in the live reef food fish trade. Overexploitation, driven primarily by demand from Chinese communities, led to its ‘Endangered’ status and CITES Appendix II listing in 2004. Hong Kong is the global import and consumer hub for this species. A Licence to Possess system for CITES is implemented in the city to regulate the quota of live wild-sourced CITES specimens, including humphead wrasse, held at each registered trading premise and ensure traceability through documentation. However, the absence of identification and tagging systems to distinguish legally traded from illegally sourced individuals is a critical CITES enforcement loophole, allowing traders to launder illegally imported fish provided the total number on their premises remains within the licensed quota. To address this, a photo identification system utilizing the unique complex facial patterns of humphead wrasse was established enabling enforcement officers to detect possible laundering by monitoring individual fish at retail outlets.Methods and resultsDeep learning models were developed for facial pattern extraction and comparison to enhance efficiency and accuracy. A YOLOv8-based extraction model achieved a 99% success rate in extracting both left and right facial patterns. A ResNet-50-based convolutional neural network retrained using a triplet loss function for individual identification, achieved top-1, top-3, and top-5 accuracies of 79.73%, 95.95%, and 100%, respectively, further characterized by a mean rank of 1.797 (median = 1, mode = 1, S.D. = 0.86) for correct comparisons with appropriate images. The ‘Saving Face’ mobile application integrates these models, enabling officers to photograph and upload humphead wrasse images during inspections to a centralized database. The application compares and detects changes in fish individuals at each location. Discrepancies between detected changes and transaction documentation raise red flags for potential illegal trade, prompting further investigation. The system is also designed for use by researchers and citizen scientists.DiscussionThis novel solution seeks to address a critical CITES enforcement loophole and shows potential for research and citizen science initiatives. The beta version of ‘Saving Face’ is available, and general public users can contribute supplementary information for enforcement and continuous model optimization. This new photo identification approach developed against wildlife trafficking using unique body markings is potentially adaptable to other threatened species. |
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| ISSN: | 2296-701X |