3D Face Reconstruction Techniques: Passive Methods
Sincy John1, Ajit Danti2
1Sincy John, Department of Computer Science & Engineering, Faculty of Engineering, Christ (Deemed to be University), Bangalore, India.
2Ajit Danti, Department of Computer Science & Engineering, Faculty of Engineering, Christ (Deemed to be University), Bangalore, India.
Manuscript received on 16 March 2019 | Revised Manuscript received on 20 March 2019 | Manuscript published on 30 July 2019 | PP: 4354-4364 | Volume-8 Issue-2, July 2019 | Retrieval Number: B3152078219/19©BEIESP | DOI: 10.35940/ijrte.B3152.078219
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In the recent literature, 3D face reconstruction received wide interest and has become one of the significant areas of research. 3D face reconstruction provides in depth details on geometrics, texture and color of the face, which are utilized in different applications. It supports a multitude of applications, ranging from face recognition and surveillance to medical imaging, gaming, animation, and virtual reality. This paper attempts to consolidate the research works that have happened in the history of 3D face reconstruction. Also, we try to classify the existing methods based on the input for the process. The databases used in the recent works are discussed and the performance evaluation of methods on different databases is analyzed. The challenges addressed in recent studies are mainly focused on the faster reconstruction of 3D Images, improved accuracy of reconstructed images, human pose identification, image reproduction with higher resolution. Researchers have also tried to address occlusion related problems. Passive methods, used by different researchers are analyzed and their effects on different parameters are discussed in this work. Finally, possible future areas for improvement in terms of reconstructions are presented for the benefit of researchers.
Index Terms: 3D Face Modeling, 3D Face Reconstruction, Computer Vision, Machine Learning, Image Processing.
Scope of the Article: Machine Learning