Descripción:

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.

Tipo de publicación: Book Chapter

Publicado en: Intelligent Systems and Applications

Descripción:

This study explores the use of transfer learning (TL) with computational neural networks (CNNs) to address the challenge of forensic tattoo identification. Tattoos are distinctive markers that play a crucial role in personal identification in forensic investigations. Traditional manual methods for tattoo identification are often time consuming and prone to errors, underscoring the need for automated and reliable alternatives. Pre-trained CNN models, using TL, have shown potential in similar image classification tasks. To evaluate their applicability in tattoo detection, eight pre-trained models were tested: MobileNetV2, Xception, NASNetMobile, InceptionV3, VGG16, DenseNet121, ResNet50 and EfficientNetB0 on a balanced dataset of 1,000 tattoo images, enhanced with data augmentation techniques. The models were trained using a fixed 70%-15%-15% split for training, validation, and testing. The data set was randomly shuffled before splitting to minimize bias in the training process. Although cross-validation is a common approach in machine learning, we opted for a fixed split to maintain consistency in model evaluation. The results indicate that MobileNetV2 achieved the highest accuracy at 99.9%, followed by DenseNet121 (99.7%) and Xception (99.4%). NASNetMobile also performed well, with an accuracy of 99%. InceptionV3 and VGG16 demonstrated moderate precision levels (97.5% and 95.4%, respectively), while ResNet50 and EfficientNetB0 achieved lower precision levels of 83.1% and 70.3%, respectively. Based on these results, MobileNetV2, DenseNet121, and Xception emerged as the most effective models were evaluated based on precision, precision, recall, F1 score and computational efficiency. This evaluation provides a comparative analysis of TL-based CNN models, offering insights into their performance and resource requirements for forensic tattoo identification.

Tipo de publicación: Book Chapter

Publicado en: Emerging Trends in Information Systems and Technologies