Cited By
View all- Zhang CPorto ARolfe SKocatulum AMaga A(2022)Automated landmarking via multiple templatesPLOS ONE10.1371/journal.pone.027803517:12(e0278035)Online publication date: 1-Dec-2022
A Non-template Based Automatic Landmarking on 3D Face Data
Pages 212 - 216
Abstract
The standard initial stage for the extraction of information from human face image data is the detection of key anatomical landmarks, which is a vital stage for several face recognition, facial analysis and synthesis applications. Locating facial landmarks in images is an important task in image processing, and detecting these landmarks automatically still remains a challenge. The appearance of facial landmarks may vary tremendously due to facial expressions and pose variations. Detecting and localising landmarks from raw face data are often performed manually by trained and experienced scientists or clinicians, and this process is laborious and tedious. In order to overcome these challenges, a novel non-template based automatic landmarking method on 3D face data is presented. The geometric approach is employed through utilising the surface curvatures, primitive surfaces information to detect and extract potential features. Subsequently, K-means clustering is applied to categorise and obtain the centroid of extracted features, which is later use to estimate and localise facial landmarks. The overall performance and accuracy of the proposed approach demonstrates the effectiveness and robustness of its method. Results of 95.6% of accurately locating the feature proved the effectiveness of the algorithm.
References
[1]
Çeliktutan, O., Ulukaya, S. and Sankur, B. 2013. A comparative study of face landmarking techniques. In EURASIP Journal on Image and Video Processing, 1 (2013), 13--40.
[2]
Gupta, S., Markey, M. K. and, Bovik, A. C. 2010. Anthropometric 3D face recognition. In International journal of computer vision, 90, 3 (2010), 331--349.
[3]
Hermann, N. V., Darvann, T. A., Larsen, P., Lindholm, P., Andersen, M., and Kreiborg, S. 2016. A pilot study on the influence of facial expression on measurements in three-dimensional digital surfaces of the face in infants with cleft lip and palate. In Cleft Palate-Craniofacial Journal. DOI=https://doi.org/10.1597/14-142
[4]
Wang, R. H., Ho, C. T., Lin, H. H., and Lo, L. J. 2019. Three-dimensional cephalometry for orthognathic planning: Normative data and analyses. In Journal of the Formosan Medical Association. DOI=https://doi.org/10.1016/j.jfma.2019.04.001
[5]
Almukhtar, A., Khambay, B., Ayoub, A., Ju, X., Al-Hiyali, A., Macdonald, J., Goto, T. 2015. "Direct DICOM slice landmarking" A novel research technique to quantify skeletal changes in orthognathic surgery. In PLoS ONE. DOI=https://doi.org/10.1371/journal.pone.0131540
[6]
Papatheodorou, T. and Rueckert, D. 2005. Evaluation of 3d face recognition using registration and PCA. In International Conference on Audio and Video-based Biometric Person Authentication, 3546 (2005).
[7]
Besl, P. J. and Jain, R. C. 1988. Segmentation through variable-order surface fitting. In IEEE Transactions on Pattern Analysis and Machine Intelligence. 10,2 (1988), 167--192.
[8]
Moreno, A. B., Sánchez, Á., Vélez, J. F., and Díaz, F. J. 2003. Face recognition using 3D surface-extracted descriptors. Signs. In Irish Machine Vision and Image Processing Conference.
[9]
Gordon, G. G. 1992. Face recognition based on depth and curvature features. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. DOI=https://doi.org/10.1109/CVPR.1992.
[10]
Colombo, A., Cusano, C., and Schettini, R. 2006. 3D face detection using curvature analysis. In Pattern Recognition. DOI=https://doi.org/10.1016/j.patcog.2005.09.009
[11]
Chang, K. I., Bowyer, K. W. and Flynn, P. J. 2006. Multiple nose region matching for 3D face recognition under varying facial expression. In IEEE Transactions on Pattern Analysis and Machine Intelligence. DOI= https://doi.org/10.1109/TPAMI.2006.210.
[12]
Koenderink, J. J. and van Doorn, A. J. 1992. Surface shape and curvature scales. In Image and vision computing, 10, 8 (1992), 557--564. DOI=https://doi.org/10.1016/0262-8856(92)90076-F
[13]
Xie, S., Shan, S., Chen, X., Meng, X., and Gao, W. 2009. Learned local Gabor patterns for face representation and recognition. In Signal Processing, 89, 12 (2009), 2333--2344. DOI=https://doi.org/10.1016/j.sigpro.2009.02.016
[14]
Biswas, S. and Sil, J. 2015. Facial expression recognition using modified local binary pattern. In Computational Intelligence in Data Mining, New York, NY, USA: Springer, 2 (2015), 595--604.
[15]
Žalik, K. R. (2008). An efficient k'-means clustering algorithm. In Pattern Recognition Letters. DOI=https://doi.org/10.1016/j.patrec.2008.02.014
[16]
Yin, L., Wei, X., Sun, Y., Wang, J., and Rosato, M. J. (2006). A 3D facial expression database for facial behavior research. In FGR 2006: Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition, (10-12 April 2006), 211--216. DOI=https://doi.org/10.1109/FGR.2006.6.
[17]
Mian, A. S., Bennamoun, M., and Owens, R. 2007. An efficient multimodal 2D-3D hybrid approach to automatic face recognition. In IEEE Transactions on Pattern Analysis and Machine Intelligence, 29, 11, (2007), 1927--1943. DOI=https://doi.org/10.1109/TPAMI.2007.1105
[18]
Wang, H., Hu, J., and Deng, W. 2018. Face Feature Extraction: A Complete Review. In IEEE Access. DOI=https://doi.org/10.1109/ACCESS.2017.2784842
[19]
Zheng, P., Belaton, B., Liao, I. Y., and Rajion, Z. A. 2017. A functional pipeline framework for landmark identification on 3D surface extracted from volumetric data. In PLoS One, 12(11), e0187558. DOI=10.1371/journal.pone.0187558
[20]
Farhang, Y. 2017. Face extraction from image based on K-means clustering algorithms. In International Journal of Advanced Computer Science and Applications, 8, 9 (2017), 96--107. DOI=https://doi.org/10.14569/ijacsa.2017.080914
[21]
Xiao, S., Yan, S., and Kassim, A. A. 2016. Facial Landmark Detection via Progressive Initialization. In Proceedings of the IEEE International Conference on Computer Vision, (February 2016), 986--993. DOI=https://doi.org/10.1109/ICCVW.2015.130
[22]
Gupta, S., Markey, M. K., and Bovik, A. C. 2010. Anthropometric 3D face recognition. In International Journal of Computer Vision. DOI=https://doi.org/10.1007/s11263-010-0360-8
[23]
Szeptycki, P., Ardabilian, M., and Chen, L. 2009. A coarse-to-fine curvature analysis-based rotation invariant 3D face landmarking. In IEEE 3rd International Conference on Biometrics: Theory, Applications and Systems, BTAS 2009. DOI=https://doi.org/10.1109/BTAS.2009.5339052
[24]
Lemaire, P., Ben Amor, B., Ardabilian, M., Chen, L., and Daoudi, M. 2011. Fully automatic 3D facial expression recognition using a region-based approach. DOI= 10.1145/2072572.2072589.
[25]
Dibeklioğlu, H., Salah, A. A. and Akarun, L. 2008. 3D facial landmarking under expression, pose, and occlusion variations. In BTAS 2008 - IEEE 2nd International Conference on Biometrics: Theory, Applications and Systems. DOI=10.1109/BTAS.2008.4699324.
[26]
MacQueen, J. 1967. Some Methods for classification and Analysis of Multivariate Observations. In 5th Berkeley Symposium on Mathematical Statistics and Probability 1967. DOI=citeulike-article-id:6083430.
[27]
Berretti, S., Werghi, N., del Bimbo, A., and Pala, P. 2014. Selecting stable keypoints and local descriptors for person identification using 3D face scans. In Visual Computer. DOI=https://doi.org/10.1007/s00371-014-0932-7
[28]
Salti, S., Tombari, F. and Di Stefano, L. 2011. A performance evaluation of 3d keypoint detectors. In Proceedings - 2011 International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission, 3DIMPVT 2011. DOI=10.1109/3DIMPVT.2011.37.
[29]
Khadhraoui, T., Benzarti, F. and Amiri, H. 2014. A novel approach to nose-tip and eye-corners detection using HK-classification in case of 3D face analysis. In 2014 World Congress on Computer Applications and Information Systems, WCCAIS 2014. DOI=10.1109/WCCAIS.2014.6916642.
Index Terms
- A Non-template Based Automatic Landmarking on 3D Face Data
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Published In
December 2019
270 pages
ISBN:9781450376822
DOI:10.1145/3376067
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In-Cooperation
- Shanghai Jiao Tong University: Shanghai Jiao Tong University
- Xidian University
- TU: Tianjin University
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 25 February 2020
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ICVIP 2019
ICVIP 2019: 2019 the 3rd International Conference on Video and Image Processing
December 20 - 23, 2019
Shanghai, China
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Cited By
View all- Zhang CPorto ARolfe SKocatulum AMaga A(2022)Automated landmarking via multiple templatesPLOS ONE10.1371/journal.pone.027803517:12(e0278035)Online publication date: 1-Dec-2022
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