Abstract
In a catastrophe, the conventional biological characteristics of the victims will be destroyed. Forensic odontology is the main method to identify the victims. Estimating the gender of the victims has a significant meaning and can greatly help identify the victims. In this paper, we propose a new automatic method to the gender estimation from panoramic dental X-ray images based on improved convolutional neural network with multiple feature fusion module. Our dataset includes 19,976 panoramic dental X-ray images from Chinese patients. The method we propose can estimate 142 images per second on the conventional computing equipment and it achieves state-of-the-art performance, accuracy of 94.6% ± 0.58%, in our dataset. Our model is interpreted by perturbation-based forward propagation approaches, and the results show that focus of our method on the area of mandible and teeth is reliable which is in accordance with forensic practice.
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Acknowledgements
This work was supported in part by National Natural Science Foundation of China (No. 61871277), in part by Sichuan Science and Technology Program (No. 2019YFH0193), and in part by Chengdu Science and Technology Program (No. 2018-YF05-00069-SN).
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Ke, W., Fan, F., Liao, P. et al. Biological Gender Estimation from Panoramic Dental X-ray Images Based on Multiple Feature Fusion Model. Sens Imaging 21, 54 (2020). https://doi.org/10.1007/s11220-020-00320-4
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DOI: https://doi.org/10.1007/s11220-020-00320-4