Automatic Face Recognition for Forensic Identification of Persons Deceased in Humanitarian Emergencies
Ruggero Donida Labati,D. De Angelis,Barbara Bertoglio,Cristina Cattaneo,Fabio Scotti,Vincenzo Piuri +5 more
- pp 1-6
TLDR
In this article, the authors proposed a recognition methodology and analyzed the accuracy of different biometric methods based on deep learning strategies for a real study case, in which the considered data regard recent deaths of migrants in Mediterranean Sea.Abstract:
Forensic scientists often need to identify deceased people. The identification process mainly consists of the analysis of the DNA, dental records, and physical appearance. In humanitarian emergencies, the antemortem documentation needed for forensic analyses may be limited. In this context, face recognition plays a relevant role since antemortem pictures of missing persons are commonly made available by their families. Therefore, automatic recognition systems could be of paramount importance for reducing the search time in databases of face images and for providing a second opinion to the scientists. However, there are only few preliminary studies on automatic face recognition methods for forensic applications, and none of the works in the literature consider problems related to humanitarian emergencies. In this paper, we propose the first study on automatic face recognition for humanitarian emergencies. Specifically, we propose a recognition methodology and we analyze the accuracy of different biometric methods based on deep learning strategies for a real study case. In particular, the considered data regard recent deaths of migrants in Mediterranean Sea. The obtained results are satisfactory, and suggest that automatic recognition methods based on deep learning strategies could be effectively adopted as support tools for forensic identification.read more
References
More filters
Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Journal ArticleDOI
Squeeze-and-Excitation Networks
TL;DR: This work proposes a novel architectural unit, which is term the "Squeeze-and-Excitation" (SE) block, that adaptively recalibrates channel-wise feature responses by explicitly modelling interdependencies between channels and finds that SE blocks produce significant performance improvements for existing state-of-the-art deep architectures at minimal additional computational cost.
Proceedings ArticleDOI
FaceNet: A unified embedding for face recognition and clustering
TL;DR: A system that directly learns a mapping from face images to a compact Euclidean space where distances directly correspond to a measure offace similarity, and achieves state-of-the-art face recognition performance using only 128-bytes perface.
Proceedings ArticleDOI
Deep face recognition
TL;DR: It is shown how a very large scale dataset can be assembled by a combination of automation and human in the loop, and the trade off between data purity and time is discussed.
Journal ArticleDOI
Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks
TL;DR: Zhang et al. as mentioned in this paper proposed a deep cascaded multitask framework that exploits the inherent correlation between detection and alignment to boost up their performance, which leverages a cascaded architecture with three stages of carefully designed deep convolutional networks to predict face and landmark location in a coarse-to-fine manner.