A Fully Automatic Framework for Prediction of 3D Facial Rejuvenation

Research output: Chapter in Book/Conference paperConference paper

Abstract

How will I look afterwards? is a common question asked by the patients undergoing a cosmetic procedure. Cosmetic practitioners at present can only offer subjective and descriptive replies. This subjective prediction is a serious concern for patients undergoing cosmetic treatment and therefore necessitates the development of automatic techniques for facial quantification. This paper proposes a novel machine learning approach to quantify and predict the outcome of 3D facial rejuvenation prior to actual cosmetic procedure. The facial rejuvenation prediction results are achieved by estimating the dermal filler volume in 3D faces. This involves estimation of structural changes in 3D
face images and to learn underlying structural mapping. Our preliminary experimental results show that the proposed model achieves superior prediction accuracy on real world dataset compared to baseline methods. The computational time analysis shows that the proposed technique is very efficient (at test time) which makes it suitable for real time applications.
Original languageEnglish
Title of host publication2018 International Conference on Image and Vision Computing New Zealand (IVCNZ)
PublisherIEEE, Institute of Electrical and Electronics Engineers
ISBN (Electronic)9781728101255
DOIs
Publication statusPublished - 4 Feb 2019
Event2018 International Conference on Image and Vision Computing New Zealand (IVCNZ) - Auckland, New Zealand
Duration: 19 Nov 201821 Nov 2018

Conference

Conference2018 International Conference on Image and Vision Computing New Zealand (IVCNZ)
CountryNew Zealand
CityAuckland
Period19/11/1821/11/18

Fingerprint

Cosmetics
Learning systems
Fillers

Cite this

Shah, S., & Bennamoun, M. (2019). A Fully Automatic Framework for Prediction of 3D Facial Rejuvenation. In 2018 International Conference on Image and Vision Computing New Zealand (IVCNZ) [ 8634657] IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IVCNZ.2018.8634657
Shah, Syed ; Bennamoun, Mohammed. / A Fully Automatic Framework for Prediction of 3D Facial Rejuvenation. 2018 International Conference on Image and Vision Computing New Zealand (IVCNZ). IEEE, Institute of Electrical and Electronics Engineers, 2019.
@inproceedings{6390543d81a243c091c9600c7589f915,
title = "A Fully Automatic Framework for Prediction of 3D Facial Rejuvenation",
abstract = "How will I look afterwards? is a common question asked by the patients undergoing a cosmetic procedure. Cosmetic practitioners at present can only offer subjective and descriptive replies. This subjective prediction is a serious concern for patients undergoing cosmetic treatment and therefore necessitates the development of automatic techniques for facial quantification. This paper proposes a novel machine learning approach to quantify and predict the outcome of 3D facial rejuvenation prior to actual cosmetic procedure. The facial rejuvenation prediction results are achieved by estimating the dermal filler volume in 3D faces. This involves estimation of structural changes in 3Dface images and to learn underlying structural mapping. Our preliminary experimental results show that the proposed model achieves superior prediction accuracy on real world dataset compared to baseline methods. The computational time analysis shows that the proposed technique is very efficient (at test time) which makes it suitable for real time applications.",
author = "Syed Shah and Mohammed Bennamoun",
year = "2019",
month = "2",
day = "4",
doi = "10.1109/IVCNZ.2018.8634657",
language = "English",
booktitle = "2018 International Conference on Image and Vision Computing New Zealand (IVCNZ)",
publisher = "IEEE, Institute of Electrical and Electronics Engineers",
address = "United States",

}

Shah, S & Bennamoun, M 2019, A Fully Automatic Framework for Prediction of 3D Facial Rejuvenation. in 2018 International Conference on Image and Vision Computing New Zealand (IVCNZ)., 8634657, IEEE, Institute of Electrical and Electronics Engineers, 2018 International Conference on Image and Vision Computing New Zealand (IVCNZ), Auckland, New Zealand, 19/11/18. https://doi.org/10.1109/IVCNZ.2018.8634657

A Fully Automatic Framework for Prediction of 3D Facial Rejuvenation. / Shah, Syed; Bennamoun, Mohammed.

2018 International Conference on Image and Vision Computing New Zealand (IVCNZ). IEEE, Institute of Electrical and Electronics Engineers, 2019. 8634657.

Research output: Chapter in Book/Conference paperConference paper

TY - GEN

T1 - A Fully Automatic Framework for Prediction of 3D Facial Rejuvenation

AU - Shah, Syed

AU - Bennamoun, Mohammed

PY - 2019/2/4

Y1 - 2019/2/4

N2 - How will I look afterwards? is a common question asked by the patients undergoing a cosmetic procedure. Cosmetic practitioners at present can only offer subjective and descriptive replies. This subjective prediction is a serious concern for patients undergoing cosmetic treatment and therefore necessitates the development of automatic techniques for facial quantification. This paper proposes a novel machine learning approach to quantify and predict the outcome of 3D facial rejuvenation prior to actual cosmetic procedure. The facial rejuvenation prediction results are achieved by estimating the dermal filler volume in 3D faces. This involves estimation of structural changes in 3Dface images and to learn underlying structural mapping. Our preliminary experimental results show that the proposed model achieves superior prediction accuracy on real world dataset compared to baseline methods. The computational time analysis shows that the proposed technique is very efficient (at test time) which makes it suitable for real time applications.

AB - How will I look afterwards? is a common question asked by the patients undergoing a cosmetic procedure. Cosmetic practitioners at present can only offer subjective and descriptive replies. This subjective prediction is a serious concern for patients undergoing cosmetic treatment and therefore necessitates the development of automatic techniques for facial quantification. This paper proposes a novel machine learning approach to quantify and predict the outcome of 3D facial rejuvenation prior to actual cosmetic procedure. The facial rejuvenation prediction results are achieved by estimating the dermal filler volume in 3D faces. This involves estimation of structural changes in 3Dface images and to learn underlying structural mapping. Our preliminary experimental results show that the proposed model achieves superior prediction accuracy on real world dataset compared to baseline methods. The computational time analysis shows that the proposed technique is very efficient (at test time) which makes it suitable for real time applications.

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85062770602&origin=resultslist&sort=plf-f&src=s&st1=A+Fully+Automatic+Framework+for+Prediction+of+3D+Facial+Rejuvenation&st2=&sid=88747d2b8ae3d6af084a7a5a55233c10&sot=b&sdt=b&sl=83&s=TITLE-ABS-KEY%28A+Fully+Automatic+Framework+for+Prediction+of+3D+Facial+Rejuvenation%29&relpos=0&citeCnt=0&searchTerm=

U2 - 10.1109/IVCNZ.2018.8634657

DO - 10.1109/IVCNZ.2018.8634657

M3 - Conference paper

BT - 2018 International Conference on Image and Vision Computing New Zealand (IVCNZ)

PB - IEEE, Institute of Electrical and Electronics Engineers

ER -

Shah S, Bennamoun M. A Fully Automatic Framework for Prediction of 3D Facial Rejuvenation. In 2018 International Conference on Image and Vision Computing New Zealand (IVCNZ). IEEE, Institute of Electrical and Electronics Engineers. 2019. 8634657 https://doi.org/10.1109/IVCNZ.2018.8634657