Automatic discovery of connections between Vietnamese's anthropometric features

VIETNAM NATIONAL UNIVERSITY, HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGY DINH QUANG HUY AUTOMATIC DISCOVERY OF CONNECTIONS BETWEEN VIETNAMESE’S ANTHROPOMETRIC FEATURES MASTER’S THESIS Hanoi – 2010 VIETNAM NATIONAL UNIVERSITY, HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGY DINH QUANG HUY AUTOMATIC DISCOVERY OF CONNECTIONS BETWEEN VIETNAMESE’S ANTHROPOMETRIC FEATURES Branch: Information Technology Major: Computer Science Code: 60 48 01 MASTER’S THESIS SUPERVISED BY: Assoc Prof BUI THE DUY Hanoi – 2010 Abstract Long time ago, when people found a skeleton, it was hard to determine who the victim was However, people are trying to find a way to solve this problem due to its demands and importance Several methods have been introduced for identifying deceased persons, some more effective than others Facial reconstruction is one of them It is a work of recreating the face of a person from his skeletal remains At the first days, facial reconstruction is done using clay, where a skillful experts who understand the structure of skull and skin very well to use clay to build up the depth of tissue on the skull to that of a living individual Later, this method is computerized and people tend to develop 3D facial reconstruction systems In the facial reconstruction systems, the most important issue is to predict the soft tissue depths at every location or some locations Most researches try to obtain a database of soft tissue thicknesses at facial landmarks, and store the average thickness for every landmark When performing the reconstruction, these thicknesses are referenced, and the face is built based on the skull model Their approaches have some problems in data collecting, and they not make use of the discovered skull to predict the thicknesses Therefore, the accuracy is very low and most of the time, they need to manually modify the model generated from the system a lot in order to receive a suitable face Realizing that the soft tissue thickness and some other anthropometric features may have some relationships with the skull shape, we propose a method for automatic discovery of these connections We first collect data using the CT technique which is the most accurate method at the moment After that, we try some machine learning techniques on the data to see the performance The evaluations and comparison with other approaches are also given in the thesis ii Table of Contents Introduction 1.1 Overview and Motivation 1.2 Our Contributions 1.3 Thesis Organization 1 2 Background 2.1 Previous Work in Facial Reconstruction From Skulls 2.1.1 2D Reconstruction 2.1.2 Manual 3D Reconstruction 2.1.3 Computer-Aided Reconstruction 2.2 Facial Reconstruction Systems 2.2.1 System developed by Bjorn Anderson, Martin Valfridsson in 2005 2.2.2 System developed by Kolja Kăahler and Jăorg Haber 2.2.3 FACES - software developed by Salerno University, Italy 2.3 Facial Landmarks 2.4 Important Facial Features 2.4.1 Ears 2.4.2 Eyes 2.4.3 Nose 2.4.4 Lips 2.5 Soft tissue thickness studies 2.6 Available Soft Tissue Thickness Data 4 10 10 12 13 13 13 13 13 15 Automatic discovery of connections between Vietnamese’s anthropometric features 16 3.1 Data description 16 3.2 Data collecting 19 iii iv TABLE OF CONTENTS 3.3 3.4 Discovery of anthropometric relationships using linear regression Discovery of anthropometric relationships using neural networks 3.4.1 Select network structure 3.4.2 Initialize and train the network 23 25 25 26 Evaluation and Result 29 Conclusions and Future Work 35 List of Figures 2.1 Matching skull into drawing portrait 2.2 Matching skull into a picture 2.3 Successful clay reconstrion by LSU Faces Lab 2.4 Process of Reconstruction using volumetric data 2.5 Result of Bjorn Anderson and Martin Valfridsson’s reconstruction 2.6 Facial Reconstruction Diagram by FACES 11 2.7 Facial landmarks Location 11 3.1 Phillip MX8000D CT Scanner 19 3.2 CT images 20 3.3 Head CT image taken with sagittal plane 20 3.4 Head CT image taken with verticle plane that goes through the middle of the left eye socket 21 3.5 Head CT image taken with vertical plane that goes through the forehead 22 3.6 Head CT image taken with horizontal plane that goes through the zygion landmarks 22 3.7 Head CT image taken with horizontal plane that goes through the gonion landmarks 23 3.8 Example of linear regression 24 3.9 A feed-forward network with a single output layer (a) and with one hidden layer and one output layer (b) 26 3.10 A recurrent network with hidden neurons 26 3.11 Neural network structure used in the study 27 4.1 Regression results obtained by ten-fold cross validation for pronasale thickness using (a) neural network model and (b) linear regression model 31 v vi LIST OF FIGURES 4.2 4.5 4.3 4.4 4.6 4.7 4.8 Regression results obtained by ten-fold cross validation for nose length using (a) neural network model and (b) linear regression model Regression results obtained by ten-fold cross validation for upper lip border using (a) neural network model and (b) linear regression model Regression results obtained by ten-fold cross validation for nose height using (a) neural network model and (b) linear regression model Regression results obtained by ten-fold cross validation for pupilpupil distance using (a) neural network model and (b) linear regression model Regression results obtained by ten-fold cross validation for lower lip border using (a) neural network model and (b) linear regression model Facial Reconstruction Result Using Linear Regression Equations Matching the face and the skull 31 31 32 32 32 33 33 List of Tables 2.1 List of Facial Landmarks 12 3.1 3.2 Input Data Fields 17 Target Data Fields 18 4.1 MSE values for ’average method’ (AVG), Linear Regression (LR), and Neural Network (NN) The best performance is in boldface 30 Equations for linear correlation between input and output, with the corresponding MSE when applied with the whole data set In the equations, x is the input and y is the output 34 4.2 vii Abbreviations 3D Three-dimensional CT Computed Tomography MRI Magnetic Resonance Imaging 2D Two-dimensional RBF Radial Basis Functions MSE Mean Square Error viii Chapter Introduction 1.1 Overview and Motivation Facial reconstruction is the work of recreating the face of an individual from his discovered skull This process is mainly used in criminal investigations to facilitate victim identification when no other means are available Besides, facial reconstruction is also used in archaeology to verify the remains of historic figures or in anthropology to approximate the look of prehistoric hominids People have been recreating the face of an unidentified individual from their discovered skulls for nearly a hundred years At the first days, facial reconstruction is done using clay This method requires skillful experts who understand the structure of skull and skin very well to use clay to build up the depth of tissue on the skull to that of a living individual The experts first place the landmark dowels on the pre-defined craniofacial landmarks on the skull After that, clay is applied and the expert interpolates with clay between the landmark dowels to build up the skin This method is called the Krogman method [Kro46] and is still used in non-automatic forensic facial reconstruction now The expert skill and amount of time required have motivated researchers to try to computerize the technique A well-designed computer-aided facial reconstruction system has many advantages, including great reduction in time consumption Using such a system, we can produce several possible facial models from a given skull by using parameters determining the person’s age, weight, and gender Recently, the rapid development of 3D equipments and technology enable us to advance into this field of research A lot of computerized methods for 3D facial ... Organization of soft tissue thickness studies and available soft tissue thickness data Chapter describes the proposed method of automatic discovery of connections between Vietnameses anthropometric features. .. of a Master thesis, we propose a method for automatic discovery of connections between anthropometric features such as tissue thicknesses, distance between two pupils, nose height and the skull... NATIONAL UNIVERSITY, HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGY DINH QUANG HUY AUTOMATIC DISCOVERY OF CONNECTIONS BETWEEN VIETNAMESE’S ANTHROPOMETRIC FEATURES Branch: Information Technology