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3D Point Cloud Face Reconstruction From 2d Image Using Pixel Density Technique

3D face reconstruction has been broadly studied and used in many fields. The aim of this project is to construct a three dimensional (3D) point cloud face from two dimensional (2D) images. When a 2D image is uploaded into the system, Face detection function detects the face from the 2D image. Then,...

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Main Author: Oh, Yeet Chin
Format: Academic Exercise
Language:English
Published: 2014
Subjects:
Q Science (General)
Online Access:https://eprints.ums.edu.my/id/eprint/17529/1/3D%20Point%20Cloud%20Face%20Reconstruction%20From%202d%20Image%20Using%20Pixel%20Density%20Technique.pdf
https://eprints.ums.edu.my/id/eprint/17529/
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spelling my.ums.eprints.175292017-11-16T08:20:00Z https://eprints.ums.edu.my/id/eprint/17529/ 3D Point Cloud Face Reconstruction From 2d Image Using Pixel Density Technique Oh, Yeet Chin Q Science (General) 3D face reconstruction has been broadly studied and used in many fields. The aim of this project is to construct a three dimensional (3D) point cloud face from two dimensional (2D) images. When a 2D image is uploaded into the system, Face detection function detects the face from the 2D image. Then, the Linear Pixel Shuffling technique is used to extract the pixel from the image. After that, the depth data is calculated. In this system, GUI menu is used so that users can interact with the system. There are other alternative choices to allow the user to capture a new photo instead using the only default photo. The basic transformation like, rotation, cell size and scale is called in the GUI menu. There are three experiments conducted to test the accuracy of the 3D model. The first test is the brightness test. During the test, three same images but varying in the level of brightness is used to compare the differences in the 3D reconstruction of the face. Through this experiment, sufficient amounts of brightness such as < 40%, 40%-60% and > 60% are needed to get an accurate result. The cell size test is carried out in the second experiment. In this test, as the cell size exceeds 6, the accuracy of the 3D model becomes lower. Furthermore, the shape of the 3D model will become cubic-look and unclear. Lastly, the memory test is examined to check the efficiency of the system. The memory consumption is low, 30-60 MB because there are not much complicated a lgorithm present in the system.In the future work, the 3D face model not only can be done by reconstructing the 3D face but also other objects. Besides, a more realistic 3D face model can be constructed instead of only construct in point cloud form. 2014 Academic Exercise NonPeerReviewed text en https://eprints.ums.edu.my/id/eprint/17529/1/3D%20Point%20Cloud%20Face%20Reconstruction%20From%202d%20Image%20Using%20Pixel%20Density%20Technique.pdf Oh, Yeet Chin (2014) 3D Point Cloud Face Reconstruction From 2d Image Using Pixel Density Technique. Universiti Malaysia Sabah. (Unpublished)
institution Universiti Malaysia Sabah
building UMS Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sabah
content_source UMS Institutional Repository
url_provider http://eprints.ums.edu.my/
language English
topic Q Science (General)
spellingShingle Q Science (General)
Oh, Yeet Chin
3D Point Cloud Face Reconstruction From 2d Image Using Pixel Density Technique
description 3D face reconstruction has been broadly studied and used in many fields. The aim of this project is to construct a three dimensional (3D) point cloud face from two dimensional (2D) images. When a 2D image is uploaded into the system, Face detection function detects the face from the 2D image. Then, the Linear Pixel Shuffling technique is used to extract the pixel from the image. After that, the depth data is calculated. In this system, GUI menu is used so that users can interact with the system. There are other alternative choices to allow the user to capture a new photo instead using the only default photo. The basic transformation like, rotation, cell size and scale is called in the GUI menu. There are three experiments conducted to test the accuracy of the 3D model. The first test is the brightness test. During the test, three same images but varying in the level of brightness is used to compare the differences in the 3D reconstruction of the face. Through this experiment, sufficient amounts of brightness such as < 40%, 40%-60% and > 60% are needed to get an accurate result. The cell size test is carried out in the second experiment. In this test, as the cell size exceeds 6, the accuracy of the 3D model becomes lower. Furthermore, the shape of the 3D model will become cubic-look and unclear. Lastly, the memory test is examined to check the efficiency of the system. The memory consumption is low, 30-60 MB because there are not much complicated a lgorithm present in the system.In the future work, the 3D face model not only can be done by reconstructing the 3D face but also other objects. Besides, a more realistic 3D face model can be constructed instead of only construct in point cloud form.
format Academic Exercise
author Oh, Yeet Chin
author_facet Oh, Yeet Chin
author_sort Oh, Yeet Chin
title 3D Point Cloud Face Reconstruction From 2d Image Using Pixel Density Technique
title_short 3D Point Cloud Face Reconstruction From 2d Image Using Pixel Density Technique
title_full 3D Point Cloud Face Reconstruction From 2d Image Using Pixel Density Technique
title_fullStr 3D Point Cloud Face Reconstruction From 2d Image Using Pixel Density Technique
title_full_unstemmed 3D Point Cloud Face Reconstruction From 2d Image Using Pixel Density Technique
title_sort 3d point cloud face reconstruction from 2d image using pixel density technique
publishDate 2014
url https://eprints.ums.edu.my/id/eprint/17529/1/3D%20Point%20Cloud%20Face%20Reconstruction%20From%202d%20Image%20Using%20Pixel%20Density%20Technique.pdf
https://eprints.ums.edu.my/id/eprint/17529/
_version_ 1760229326247165952
score 13.211869

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