Luận văn thạc sĩ VNU UET automatic discovery of connections between vietnameses 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 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com 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 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com 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 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com 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 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com 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 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com 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 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com 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 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com 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 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com Abbreviations 3D Three-dimensional CT Computed Tomography MRI Magnetic Resonance Imaging 2D Two-dimensional RBF Radial Basis Functions MSE Mean Square Error viii LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com 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 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com Chapter Automatic discovery of connections between Vietnamese’s 26 anthropometric features Figure 3.9: A feed-forward network with a single output layer (a) and with one hidden layer and one output layer (b) Figure 3.10: A recurrent network with hidden neurons structure can represent any functional relationship between inputs and outputs if the hidden layer has enough neurons [HDB96] The design of this neural network structure is shown in Figure 3.11 3.4.2 Initialize and train the network Before training the network, the weights and biases are randomly initialized The performance is different each training because of this randomly initiation The training process requires a set of inputs p and targets t and begins afterward This process tunes the values of the network’s weights and biases to optimize network performance defined by MSE function MSE between the network outputs a and LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com 3.4 Discovery of anthropometric relationships using neural networks 27 Figure 3.11: Neural network structure used in the study target outputs t is defined as follows mse = N N (ei )2 = i=1 N N (ti − )2 (3.2) i=1 In order to train the network, any optimization algorithm can be used to optimize the performance function However, some algorithms are believed to have better performance These methods use the gradient of the network performance with respect to the network weights The gradient is calculated using the backpropagation algorithm which is an efficient way to calculate the partial derivatives of the network error function with respect to the weights [Gro02] There are many training algorithms which make use of the gradients’ information supplied by the backpropagation algorithm In these algorithms, a weight update from iteration k to k + may look like wk+1 = wk + η.dk (3.3) where dk is the search direction and η is the learning rate The training algorithms are different in ways of determining the search direction and the learning rate Different algorithms might also generate different performances The fastest training functions are Levenberg-Marquardt function and QuasiNewton function However, these two methods are less efficient for large networks due to their huge resource consumption In these cases, Scaled Conjugate Gradient LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com Chapter Automatic discovery of connections between Vietnamese’s 28 anthropometric features function and Resilient Backpropagation function are better choices Since our network model is small and Levenberg-Marquardt function performs best on nonlinear regression problems, we decide to choose this function as our training function For each thickness in output, we need to obtain a prediction neural network model This can be done by let this thickness be target, and all the input data be input for the training process However, as most fields in input not have any relationship with the output thickness, the performance might be very bad We apply a simple method to increase the performance We start the training process with all the input data We train the network and record the performance over the validation set After that, we try removing one field in input data and retrain the network If the performance over the validation set this time is worse, we return the removed field Otherwise, the removed input field stays outside We continue this process until all input field is tried By this time, we have the set of good relationship input data with the output thickness, and the model that contains this relationship LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com Chapter Evaluation and Result We perform the evaluation on the dataset of males which contains 98 samples In our evaluation, we use the ten-fold cross-validation to compute the output’s MSE for the two approaches, linear regression and neural network As for neural network, the training is done several times, with the number of neurons from 10 to 20 and randomly initialized weights each time The network with best performance over the validation set is chosen to generate output for the test set We then compare these MSE with the ’average method’ in which the output thickness for all tests is simply the average of all the output in training set This ’average method’ is what is used in almost every facial reconstruction systems so far Table 4.1 shows our result and their comparisons with the average It can be seen from the table that the linear regression always give better result than the average Most of the time, neural networks generate the best result over all However, there are cases when neural network gives even worse result than average such as result for zygomatic arch (R), zygomatic (L), gonion (L), and nose height In order to deeply analysis, we try plotting results for some random output Figure 4.1, 4.2, 4.3, 4.4, 4.5, and 4.6 shows the experiment result In these figures, predicted distances are plotted against the true value For a perfect prediction, the data should fall along a 45 degree line (the Y=T line), where the outputs are equal to the targets The neural network’s values for pronasale thickness, nose length, pupil-pupil distance are close to the diagonal, indicating the prediction was good For linear regression, prediction for nose length and pupil-pupil distance seems to have good performance The other predictions are not as good, but acceptable 29 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com 30 Chapter Evaluation and Result Table 4.1: MSE values for ’average method’ (AVG), Linear Regression (LR), and Neural Network (NN) The best performance is in boldface N# 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 Output vertex trichion glabella nasion rhinion pronasale nose length subnasale upper lip border lower lip border stomion metal meton opisthooranion exocanthion (R) exocanthion (L) endocanition (R) endocantion (L) pupil-pupil supraobital (R) supraobital (L) infraobital (R) infraobital (L) zygomatic arch (R) zygomatic arch (L) zygomatic (R) zygomatic (L) porion (R) porion (L) gonion (R) gonion (L) alare (R) alare (L) lateral nasal (R) lateral nasal (L) nose height bucal (R) bucal (L) AVG 1.1914 1.2945 1.2074 0.9699 0.3886 7.9621 21.8621 6.3008 4.9468 3.1674 2.2193 4.1007 2.3685 1.8909 0.7884 0.8609 2.5804 2.6779 10.8380 0.6689 0.6859 1.4038 1.1147 0.8485 0.8857 0.8326 0.9557 3.3546 2.5552 1.0521 0.9360 2.0965 2.0342 1.9751 2.0908 4.1012 13.6992 13.9451 LR 1.0625 1.0877 1.0110 0.7571 0.3400 6.0558 10.8344 4.3927 4.3581 2.7312 1.8766 3.4298 1.9901 1.5124 0.6635 0.7121 2.0950 2.0706 4.4587 0.5533 0.5340 1.2479 0.9573 0.7432 0.7400 0.6982 0.7722 2.7241 2.0471 0.9333 0.8330 1.6396 1.5304 1.4220 1.3537 3.5995 11.2034 11.6959 NN 0.8928 1.0664 1.0706 0.7220 0.3797 5.2456 8.7059 4.6878 3.7205 2.4167 1.8168 3.3625 2.0885 1.1001 0.7084 0.8459 1.7213 2.0099 4.9687 0.4556 0.4986 1.0475 1.1920 1.6805 0.7982 0.5635 1.3729 2.9786 1.7367 0.8245 1.5443 1.5934 1.4494 1.5541 1.3495 4.5687 12.2837 11.7598 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com 31 (a) Neural network model (MSE=5.2456) (b) Linear regression model (MSE=6.0558) Figure 4.1: Regression results obtained by ten-fold cross validation for pronasale thickness using (a) neural network model and (b) linear regression model (a) Neural network model (MSE=8.7059) (b) Linear regression model (MSE=10.8344) Figure 4.2: Regression results obtained by ten-fold cross validation for nose length using (a) neural network model and (b) linear regression model (a) Neural network model (MSE=3.7205) (b) Linear regression model (MSE=4.3581) Figure 4.5: Regression results obtained by ten-fold cross validation for upper lip border using (a) neural network model and (b) linear regression model LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com 32 Chapter Evaluation and Result (a) Neural network model (MSE=4.5687) (b) Linear regression model (MSE=3.5995) Figure 4.3: Regression results obtained by ten-fold cross validation for nose height using (a) neural network model and (b) linear regression model (a) Neural network model (MSE=4.9687) (b) Linear regression model (MSE=4.4587) Figure 4.4: Regression results obtained by ten-fold cross validation for pupil-pupil distance using (a) neural network model and (b) linear regression model (a) Neural network model (MSE=2.4167) (b) Linear regression model (MSE=2.7312) Figure 4.6: Regression results obtained by ten-fold cross validation for lower lip border using (a) neural network model and (b) linear regression model LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com 33 Figure 4.7: Facial Reconstruction Result Using Linear Regression Equations Figure 4.8: Matching the face and the skull A complete linear equation for one to one correlation between input and output is shown in Table 4.2 These equations are used in our facial reconstruction system A visual result of our work is given in Figure 4.7 In this figure, the face on the left is the result of facial reconstruction from the skull in the right The facial landmarks are also shown in the skull Figure 4.8 shows how the face and skull are matched LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com 34 Chapter Evaluation and Result Table 4.2: 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 N# 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 Output vertex trichion glabella nasion rhinion pronasale nose length subnasale upper lip border lower lip border stomion metal meton opisthooranion exocanthion (R) exocanthion (L) endocanition (R) endocantion (L) pupil-pupil supraobital (R) supraobital (L) infraobital (R) infraobital (L) zygomatic arch (R) zygomatic arch (L) zygomatic (R) zygomatic (L) porion (R) porion (L) gonion (R) gonion (L) alare (R) alare (L) lateral nasal (R) lateral nasal (L) nose height bucal (R) bucal (L) Input cranial breadth cranial height forehead width (ft-ft) cranial height molar-molar base nose length n-rh bn-bn molar-molar cranial height base facial length(ba-pr) al-al al-al al-al cranial height cranial height cranial height cranial height ex-ex en-en al-al cranial height cranial height n-rh base cranial length (n-ba) base facial length(ba-pr) base facial length(ba-pr) bn-bn bn-bn al-al nasal projection al-al al-al al-al al-al base nose length bn-bn bn-bn Linear Equation y = -0.038041x + 10.8251 y = 0.07284x - 4.6447 y = 0.073272x - 2.2482 y = 0.070439x - 5.022 y = -0.036784x + 4.1817 y = 0.34191x + 5.6906 y = 1.1274x + 27.5733 y = -0.3371x + 22.5646 y = 0.12137x + 5.0732 y = 0.086747x + 1.7837 y = -0.072432x + 10.843 y = 0.18113x + 3.5632 y = 0.13342x + 1.0783 y = 0.14536x - 0.019573 y = 0.052174x - 3.2308 y = 0.054596x - 3.4705 y = 0.10877x - 8.8266 y = 0.13074x - 11.5992 y = 0.71282x - 5.9472 y = 0.083979x + 2.6781 y = 0.097903x + 1.0674 y = 0.051155x - 2.4011 y = 0.055695x - 2.9922 y = 0.070673x + 3.49 y = -0.049215x + 9.3495 y = -0.042995x + 8.556 y = -0.051253x + 9.3569 y = 0.1512x + 4.5823 y = 0.16467x + 4.1336 y = 0.088104x + 0.53022 y = 0.10235x + 3.5032 y = 0.18611x + 1.0906 y = 0.2063x + 0.33053 y = 0.23816x - 2.0783 y = 0.2648x - 3.1083 y = 0.17297x + 15.4149 y = 0.31662x + 6.9254 y = 0.31206x + 7.0209 MSE 1.1230 1.1500 1.0456 0.8300 0.3635 6.7684 11.8050 4.7101 4.5644 2.9479 2.0073 3.7307 2.1684 1.5853 0.7072 0.7609 2.2855 2.1918 4.6317 0.5880 0.5861 1.2943 1.0309 0.8013 0.7973 0.7596 0.8386 3.0310 2.1702 0.9684 0.8845 1.7071 1.6000 1.4152 1.3845 3.7506 12.2240 12.3088 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com Chapter Conclusions and Future Work Facial reconstruction is an interesting research field as it helps in many cases Researchers have been developing facial reconstruction systems to fasten the manual process and also to produce better results In this general problem, one of the most important issues is to determine the soft tissue thicknesses at landmarks on the skull However, most facial reconstruction systems neglect this issue and use the average thickness for simplicity Our research has pointed out that this ’average method’ has worse performance than our linear regression method in every case, and worse than our neural network method in most cases Our research also shows that there are relationships between the skull shape and the tissue depths and should people investigate more and more to discover these relationships However, our research has some limitation which can be improved to obtain better results The following is our future works The first possible development is to improve measurement process As can be seen from the experiments, our results show good performance for long distances such as pronasale thickness or nose length, and bad performance for short distances, due to the error appeared in the measurement process This is because the longer the distance, the less effect it receives from measurement error In addition, the thin soft tissues not depend much on the skull shape, or in other words, they not have much relationship with the metrics In addition, to define the landmarks on the CT images depends much on the skill and judgment of the people who perform the measurement, although this technique is the most accurate This method also requires a lot of time to measure and collect data We plan to apply image processing to automatic discovery of these metrics This would save a lot of time in 35 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com 36 Chapter Conclusions and Future Work measurement and might give better accuracy Another thing that needs to be noted is that, in 2009, Pascal Paysan et al [PLA+ 09] proposed a method to reconstruct the face from the skull, with the capable of tuning the weight and age attributes From this research, we know that weight and age affect the facial shape greatly Our candidates’ age and weight are within wide range of 18 to 82 and 43kg to 75kg, respectively Separating candidates into groups is very important because the relationship between features is different from this age and weight range to the others and missing this step will lead to moderate error in training and validation However, in our experiment, we could not separate the candidates into groups because the number of entries was not sufficient Separating would give even worse result In the future, we will collect more data for each group of weight and age This will improve the prediction performance significantly In addition, because our data and problem is straight forward, many other machine learning techniques can be applied such as the decision stump, support vector machines, or boosting With satisfactory results from neural network approach, it is possibly that better result can be obtained from other techniques We plan to implement and analyze result using different techniques Lastly, as different landmark configurations might lead to different results and performances, using different landmark configuration is a worth trying work This work requires additional data obtaining from CT images, however LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com Publications list Quang Huy Dinh, Thi Chau Ma, The Duy Bui, Trong Toan Nguyen, Dinh Tu Nguyen (2011) Facial soft tissue thicknesses prediction using anthropometric distances Studies in Computational Intelligence, Springer Proceedings of the 3rd Asian Conference on 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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