A deep learning approach for the forensic evaluation of sexual assault
Autor: | Kelwin Fernandes, Birgitte Schmidt Astrup, Jaime S. Cardoso |
---|---|
Jazyk: | angličtina |
Rok vydání: | 2018 |
Předmět: |
Feature engineering
Digital colposcopy 010501 environmental sciences Genital injury 01 natural sciences Ranking (information retrieval) 03 medical and health sciences 0302 clinical medicine Segmentation Image processing Artificial Intelligence Complaint Relevance (law) 030216 legal & forensic medicine Architecture 0105 earth and related environmental sciences Forensics Artificial neural network business.industry Deep learning Classification Data science Transfer learning Computer Vision and Pattern Recognition Artificial intelligence business Transfer of learning Psychology Neural networks |
Zdroj: | Fernandes, K, Cardoso, J S & Astrup, B S 2018, ' A deep learning approach for the forensic evaluation of sexual assault ', Pattern Analysis and Applications, vol. 21, no. 3, pp. 629–640 . https://doi.org/10.1007/s10044-018-0694-3 |
Popis: | Despite the existence of patterns able to discriminate between consensual and non-consensual intercourse, the relevance of genital lesions in the corroboration of a legal rape complaint is currently under debate in many countries. The testimony of the physicians when assessing these lesions has been questioned in court due to several factors (e.g., a lack of comprehensive knowledge of lesions, wide spectrum of background area, among others). Therefore, it is relevant to provide automated tools to support the decision process in an objective manner. In this work, we evaluate the performance of state-of-the-art deep learning architectures for the forensic assessment of sexual assault. We propose a deep architecture and learning strategy to tackle the class imbalance on deep learning using ranking. The proposed methodologies achieved the best results when compared with handcrafted feature engineering and with other deep architectures. |
Databáze: | OpenAIRE |
Externí odkaz: |