Título: A tatoo location problem approach using mask R -CNN network
Autores: Rodrigo Tchalski da Silva, Heitor Silvério Lopes
Resumo: Tattoos are still poorly explored as a biometric factor for human identification, especially in law enforcement, where they can play an important role in identifying criminals, victims, or other persons of interest. Tattoos are classified as soft biometrics as they are not permanent and can change over time, unlike hard biometric traits (fingerprint, iris, DNA, etc.). In this way, the main objective of this work is to apply an approach to the Mask R-CNN network with a tattoo dataset and to fine-tune the set of parameters that presented the best results in training the network. In tattoo location, the results reached an mAP of 0.893, which shows that the Mask R-CNN network has great adaptability to the tattoo environment, in addition to performing a qualitative analysis that helped to understand how the characteristics of images and annotations influence the results. We presented two new datasets for tattoo location, composed of 5,754 new annotated images. Future work will include improving the quality and volume of the databases, conducting a more in-depth study on the fine-tuning of network parameters, and developing models for other problems that make up the tattoo recognition roadmap.
Palavras-chave: Tattoo location; Mask R-CNN; Computer vision; Tattoo dataset.
Páginas: 7
Código DOI: 10.21528/CBIC2025-1192307
Artigo em PDF: CBIC_2025_paper1192307.pdf
Arquivo BibTeX:
CBIC_2025_1192307.bib