TY  - CHAP
ID  - SisLab2653
UR  - https://eprints.uet.vnu.edu.vn/eprints/id/eprint/2653/
A1  - Dinh, Quang Huy
A1  - Ma, Thi Chau
A1  - Bui, The Duy
A1  - Nguyen, Trong Toan
A1  - Nguyen, Dinh Tu
Y1  - 2011///
N2  - Predicting the face of an unidentified individual from its skeletal remains is a difficult matter. Obviously, if the soft tissue thicknesses at every location at the skull are known, we can easily rebuild the face from the skull model. Thus, the problem turns out to be predicting the soft tissue thicknesses for any given skull. With the rapid development of the computer, different techniques are being used in the community for prediction tasks and in recent years the concept of neural networks has emerged as one of them. The principal strength of the neural network is its ability to find patterns and irregularities as well as detecting multi-dimensional non-linear connections in data. In this paper, we propose a method of applying neural networks to predict the soft tissue thicknesses for facial reconstruction. We use the distances between anthropometric locations at the skull as input, and the soft tissue thicknesses as output, as this format is suitable for many machine learning mechanisms. These data is collected and measured from candidates using the Computed Tomography (CT) technique.
PB  - SpringerLink
TI  - Facial soft tissue thicknesses prediction using anthropometric distances
SP  - 117
AV  - none
EP  - 126
T2  - New Challenges for Intelligent Information and Database Systems
ER  -