A Comparative Study of Transfer Learning Models for Industrial and Forensic Applications in Automated Tattoo Detection

Tattoos have been used as biometric identifiers in forensic investigations and industrial security applications. Institutions such as the Costa Rican Judicial Investigation Organization (OIJ) use tattoo recognition as part of their identification and tracking processes. Manual methods for tattoo recognition can be time-consuming and may involve variability in interpretation. In response, this study evaluates multiple transfer learning (TL) approaches for automated tattoo detection. Seven pretrained Convolutional Neural Network (CNN) architectures were examined: MobileNet, DenseNet121, EfficientNetB0, InceptionV3, NASNetMobile, VGG16, and Xception. A dataset comprising 1,000 images was processed using standard data augmentation techniques to improve generalization. Model performance was assessed using established image classification metrics: precision, recall, F1 score, and computational efficiency. The results indicate that MobileNet and ResNet achieved 99.93% accuracy under the experimental conditions given. However, MobileNet required less inference time (3.92 min) compared to ResNet (6.35 min), suggesting potential advantages for applications with time constraints. The results highlight the behavior of the model in terms of classification accuracy and resource requirements. Further evaluation using larger and more diverse datasets is recommended, as well as testing under operational forensic conditions. This study provides a comparative analysis of pretrained CNNs and discusses their applicability in forensic and industrial contexts.