Leveraging on few-shot learning for tire pattern classification in forensics

This paper presents a novel approach for tire-pattern classification, aimed at conducting forensic analysis on tire marks discovered at crime scenes. The classification model proposed in this study accounts for the intricate and dynamic nature of tire prints found in real-world scenarios, including accident sites. To address this complexity, the classifier model was developed to harness the meta-learning capabilities of few-shot learning algorithms (learning-to-learn). The model is meticulously designed and optimized to effectively classify both tire patterns exhibited on wheels and tire-indentation marks visible on surfaces due to friction. This is achieved by employing a semantic segmentation model to extract the tire pattern marks within the image. These marks are subsequently used as a mask channel, combined with the original image, and fed into the classifier to perform classification. Overall, The proposed model follows a three-step process: (i) the Bilateral Segmentation Network is employed to derive the semantic segmentation of the tire pattern within a given image. (ii) utilizing the semantic image in conjunction with the original image, the model learns and clusters groups to generate vectors that define the relative position of the image in the test set. (iii) the model performs predictions based on these learned features.

Empirical verification demonstrates usage of semantic model to extract the tire patterns before performing classification increases the overall accuracy of classification by $\sim$4%.