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Automatic extraction of head and face boundaries and facial features

Authors:

Chao-Fa ChuangAuthors Info & Claims

Information Sciences—Informatics and Computer Science, Intelligent Systems, Applications: An International Journal, Volume 158, Issue 1

Pages 117 - 130

Published: 01 January 2004 Publication History

Abstract

This paper presents a novel approach for the extraction of human head, face and facial features. In the double-threshold method, the high-thresholded image is used to trace head boundary and the low-thresholded image is used to scan face boundary. We obtain facial features candidates and eliminate noises, and apply x- and y-projections to extract facial features such as eyes, nostrils and mouth. Because low contrast of chin occurs in some face images, its boundary cannot be completely detected. An elliptic model is used to repair it. Because of noises or clustered facial features candidates, we apply a geometric face model to locate facial features and an elliptic model to trace face boundary. The Gabor filter algorithm is adopted to locate two eyes. We have tested our algorithm on more than 100 FERET face images. Experimental results show that our algorithm can perform the extraction of human head, face and facial features successfully.

References

[1]

{1} A. Samal, P.A. Iyengar, Automatic recognition and analysis of human faces and facial expressions: a survey, Pattern Recognition 25 (1992) 65-77.

Digital Library

[2]

{2} J. Ahlberg, Facial feature extraction using deformable graphs and statistical pattern matching, in: Proceedings SSAB Annual Symposium on Image Analysis, Göteborg, Sweden, March 1999, pp. 89-92.

[3]

{3} J. Ahlberg, A system for face localization and facial feature extraction, Technical Report, no. LiTH-ISY-R-2172, Linköping University, July 1999.

[4]

{4} K. Sobottka, I. Pitas, A novel method for automatic face segmentation facial feature extraction and tracking, Signal Processing: Image Communication 12 (3) (1998) 263-281.

[5]

{5} S.-H. Jeng, H.Y. MarkLiao, C.C. Han, M.Y. Chern, Y. Liu, Facial feature detection using geometrical face model: an efficient approach, Pattern Recognition 31 (1998) 273-282.

[6]

{6} K.-W. Wong, K.-M. Lam, W.-C. Siu, An efficient algorithm for human face detection and facial feature extraction under different conditions, Pattern Recognition 34 (2001) 1993-2004.

[7]

{7} C. Lin, K.-C. Fan, Triangle-based approach to the detection of human face, Pattern Recognition 34 (2001) 1271-1284.

[8]

{8} Y.-S. Ryu, S.-Y. Oh, Automatic extraction of eye and mouth field from a face image using eignfeatures and multilayer perceptrons, Pattern Recognition 34 (2001) 2459-2466.

Digital Library

[9]

{9} K.C. Yow, R. Cipolla, Enhancing human face detection using motion and active contours, in: Proceedings 3rd Asian Conference on Computer Vision, 1, Hong Kong, 1998, pp. 515-522.

[10]

{10} A. Nikolaidis, I. Pitas, Facial feature extraction and pose determination, Pattern Recognition 33 (2000) 1783-1791.

[11]

{11} C. Xu, J.-L. Prince, Snakes, shapes, and gradient vector flow, IEEE Transactions on Image Processing 7 (3) (1998) 359-369.

Digital Library

[12]

{12} M. Kass, A. Witkin, D. Terzopoulos, Snakes: active contour models, International Journal Computer Vision (1988) 321-331.

[13]

{13} H. Wechsler, M. Kidode, A new detection technique and its implementation, IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-7, 12, December 1977, pp. 827-835.

[14]

{14} J. Hu, H. Yan, M. Sakalli, Locating head and face boundaries for head-shoulder images, Pattern Recognition 32 (1999) 1317-1333.

[15]

{15} I.R. Fasel, E.C. Smith, M.S. Bartlett, J.R. Movellan, A comparison of Gabor filter methods for automatic detection of facial landmarks, in: Proceedings IEEE International Conference on Automatic face and Gesture Recognition, May 2002, pp. 231-235.

[16]

{16} E. Hjelmås, Feature-based face recognition, in: Proceedings Norwegian Image Processing and Pattern Recognition Conference, 2000.

Cited By

Canal FMüller TMatias JScotton Gde Sa Junior APozzebon ESobieranski A(2022)A survey on facial emotion recognition techniquesInformation Sciences: an International Journal10.1016/j.ins.2021.10.005582:C(593-617)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1016/j.ins.2021.10.005
Wang SPhillips PDong ZZhang Y(2018)Intelligent facial emotion recognition based on stationary wavelet entropy and Jaya algorithmNeurocomputing10.1016/j.neucom.2017.08.015272:C(668-676)Online publication date: 10-Jan-2018
https://dl.acm.org/doi/10.1016/j.neucom.2017.08.015
Tang ZSu YEr MQi FZhang LZhou J(2015)A local binary pattern based texture descriptors for classification of tea leavesNeurocomputing10.1016/j.neucom.2015.05.024168:C(1011-1023)Online publication date: 30-Nov-2015
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Index Terms

Automatic extraction of head and face boundaries and facial features
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Interest point and salient region detections
        Object recognition
      2. Computer vision representations

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Published In

cover image Information Sciences: an International Journal

Information Sciences: an International Journal Volume 158, Issue 1

Special issue: Informatics and computer science intelligent systems applications

January 2004

284 pages

ISSN:0020-0255

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Publisher

Elsevier Science Inc.

United States

Publication History

Published: 01 January 2004

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Cited By

Canal FMüller TMatias JScotton Gde Sa Junior APozzebon ESobieranski A(2022)A survey on facial emotion recognition techniquesInformation Sciences: an International Journal10.1016/j.ins.2021.10.005582:C(593-617)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1016/j.ins.2021.10.005
Wang SPhillips PDong ZZhang Y(2018)Intelligent facial emotion recognition based on stationary wavelet entropy and Jaya algorithmNeurocomputing10.1016/j.neucom.2017.08.015272:C(668-676)Online publication date: 10-Jan-2018
https://dl.acm.org/doi/10.1016/j.neucom.2017.08.015
Tang ZSu YEr MQi FZhang LZhou J(2015)A local binary pattern based texture descriptors for classification of tea leavesNeurocomputing10.1016/j.neucom.2015.05.024168:C(1011-1023)Online publication date: 30-Nov-2015
https://dl.acm.org/doi/10.1016/j.neucom.2015.05.024
Vyas JJoshi MRaval M(2015)Automatic target image detection for morphingJournal of Visual Communication and Image Representation10.1016/j.jvcir.2014.12.00527:C(28-43)Online publication date: 1-Feb-2015
https://dl.acm.org/doi/10.1016/j.jvcir.2014.12.005
Ali HHariharan MYaacob SAdom A(2015)Facial emotion recognition using empirical mode decompositionExpert Systems with Applications: An International Journal10.1016/j.eswa.2014.08.04942:3(1261-1277)Online publication date: 15-Feb-2015
https://dl.acm.org/doi/10.1016/j.eswa.2014.08.049
Benmohamed ANeji MRamdani MWali AAlimi A(2015)FeastMultimedia Tools and Applications10.1007/s11042-014-2082-374:21(9297-9322)Online publication date: 1-Nov-2015
https://dl.acm.org/doi/10.1007/s11042-014-2082-3
Ali HHariharan MYaacob SAdom A(2014)Hybrid feature extraction for facial emotion recognitionInternational Journal of Intelligent Systems Technologies and Applications10.1504/IJISTA.2014.06517513:3(202-221)Online publication date: 1-Oct-2014
https://dl.acm.org/doi/10.1504/IJISTA.2014.065175
Giorgana GPloeger P(2012)Facial expression recognition for domestic service robotsRobot Soccer World Cup XV10.5555/2554542.2554576(353-364)Online publication date: 18-Jun-2012
https://dl.acm.org/doi/10.5555/2554542.2554576
Breuer TGiorgana Macedo GHartanto RHochgeschwender NHolz DHegger FJin ZMüller CPaulus JReckhaus MÁlvarez Ruiz JPlöger PKraetzschmar G(2012)JohnnyJournal of Intelligent and Robotic Systems10.1007/s10846-011-9608-y66:1-2(245-272)Online publication date: 1-Apr-2012
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Li DTian YWan CLiu S(2012)Facial expression feature selection based on rough setProceedings of the Third international conference on Information Computing and Applications10.1007/978-3-642-34062-8_21(159-166)Online publication date: 14-Sep-2012
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