Nose tip detection on three‐dimensional faces using pose‐invariant differential surface features
Abstract
Three‐dimensional (3D) facial data offer the potential to overcome the difficulties caused by the variation of head pose and illumination in 2D face recognition. In 3D face recognition, localisation of nose tip is essential to face normalisation, face registration and pose correction etc. Most of the existing methods of nose tip detection on 3D face deal mainly with frontal or near‐frontal poses or are rotation sensitive. Many of them are training‐based or model‐based. In this study, a novel method of nose tip detection is proposed. Using pose‐invariant differential surface features – high‐order and low‐order curvatures, it can detect nose tip on 3D faces under various poses automatically and accurately. Moreover, it does not require training and does not depend on any particular model. Experimental results on GavabDB verify the robustness and accuracy of the proposed method.
6 References
[1]
Suruliandi, A., Meena, K., Reena Rose, R.: ‘Local binary pattern and its derivatives for face recognition’, IET Comput. Vis., 2012, 6, (5), pp. 480–488
[2]
Abate, A.F., Andrea, F., Riccio, D., Sabatino, G.: ‘2D and 3D face recognition: a survey’, Pattern Recognit. Lett., 2007, 28, (14), pp. 1885–1906
[3]
Bowyer, K.W., Chang, K., Flynn, P.: ‘A survey of approaches and challenges in 3D and multi‐modal 3D + 2D face recognition’, Comput. Vis. Image Underst., 2006, 101, (1), pp. 1–15
[4]
Mian, A.S., Bennamoun, M., Owens, R.: ‘An efficient multimodal 2D‐3D hybrid approach to automatic face recognition’, IEEE Trans. Pattern Anal. Mach. Intell., 2007, 29, (11), pp. 1927–1943
[5]
Peng, X., Bennamoun, M., Mian, A.S.: ‘A training‐free nose tip detection method from face range images’, Pattern Recognit., 2011, 44, (3), pp. 544–558
[6]
Dorai, C., Jain, A.: ‘COSMOS – a representation scheme for 3D free‐form objects’, IEEE Trans. Pattern Anal. Mach. Intell., 1997, 1997, 19, (10), pp. 1115–1130
[7]
Xu, C., Tan, T., Wang, Y., Quan, L.: ‘Combining local features for robust nose location in 3D facial data’, Pattern Recognit. Lett., 2006, 27, (13), pp. 1487–1494
[8]
Colbry, D., Stockman, G., Anil, J.: ‘Detection of anchor points for 3d face verification’. IEEE Computer Society Conf. Computer Vision and Pattern Recognition‐Workshops, 2005. CVPR Workshops, 2005, pp. 118–125
[9]
Colbry, D., Stockman, G.: ‘Real‐time identification using a canonical face depth map’, IET Comput. Vis., 2009, 3, (2), pp. 74–92
[10]
Mahmood, S.A., Ghani, R.F., Kerim, A.A.: ‘Nose tip detection using shape index and energy effective for 3d face recognition’, Int. J. Mod. Eng. Res. (IJMER), 2013, 3, (5), pp. 3086–3090
[11]
Perakis, P., Passalis, G., Theoharis, T., Kakadiaris, I.: ‘3D facial landmark detection under large yaw and expression variations’, IEEE Trans. Pattern Anal. Mach. Intell., 2013, 35, (7), pp. 1552–1564
[12]
Colombo, A., Cusano, C., Schettini, R.: ‘3D face detection using curvature analysis’, Pattern Recognit., 2006, 39, (3), pp. 444–455
[13]
Chang, K.I., Bowyer, K.W., Flynn, P.J.: ‘Multiple nose region matching for 3D face recognition under varying facial expression’, IEEE Trans. Pattern Anal. Mach. Intell., 2006, 28, (10), pp. 1695–1700
[14]
Gupta, S., Markey, M.K., Bovik, A.C.: ‘Anthropometric 3D face recognition’, Int. J. Comput. Vis., 2010, 90, (3), pp. 331–349
[15]
Besl, P.J., McKay, H.D.: ‘A method for registration of 3‐d shapes’, IEEE Trans. Pattern Anal. Mach. Intell., 1992, 14, (2), pp. 239–256
[16]
Szeptycki, P., Ardabilian, M., Chen, L.: ‘A coarse‐to‐fine curvature analysis‐based rotation invariant 3D face landmarking’. Int. Conf. Biometrics: Theory, Applications and Systems, 2009, pp. 32–37
[17]
Augusto, S., Alexander, C., Flavio, P.: ‘3D curvature‐based shape descriptors for face segmentation: an anatomical‐based analysis’. ISVC, 2010, pp. 349–358
[18]
Moreno, A.B., Sanchez, A.: ‘GavabDB: a 3D face database’. Proc. Second COST Workshop on Biometrics on the Internet: Fundamentals, Advances and Applications, 2004, pp. 77–82
[19]
Adams, R., Bischof, L.: ‘Seeded region growing’, IEEE Trans. Pattern Anal. Mach. Intell., 1994, 16, (6), pp. 641–647
[20]
Thirion, J.P.: ‘The extremal mesh and the understanding of 3D surfaces’, Int. J. Comput. Vis., 1996, 19, (2), pp. 115–128
[21]
Porteous, I.R.: ‘Geometric differentiation for the intelligence of curves and surfaces’ (Cambridge University Press, Cambridge, 1994)
[22]
Belyaev, A.G., Pasko, A.A., Kunii, T.L.: ‘Ridges and ravines on implicit surfaces’. Proc. Computer Graphics Int., 1998. IEEE, 1998, pp. 530–535
[23]
Yoshizawa, S., Belyaev, A., Yokota, H., Seidel, H.P.: ‘Fast, robust, and faithful methods for detecting crest lines on meshes’, Comput. Aided Geom. Des., 2008, 25, (8), pp. 545–560
[24]
Belyaev, A.G., Anoshkina, E.V., Kunii, T.L.: ‘Ridges, ravines and singularities’. Topological modeling for visualization, 1997, pp. 375–383
[25]
Belyaev, A.G., Bogaevski, I.A., Kunii, T.L.: ‘Ridges and ravines on a surface and segmentation of range images’. Optical Science, Engineering and Instrumentation'97. Int. Society for Optics and Photonics, 1997, pp. 106–114
[26]
Goldfeather, J., Interrante, V.: ‘A novel cubic‐order algorithm for approximating principal direction vectors’, ACM Trans. Graph., 2004, 23, (1), pp. 45–63
[27]
Ohtake, Y., Belyaev, A., Seidel, H.P.: ‘Ridge‐valley lines on meshes via implicit surface fitting’, ACM Trans. Graph., 2004, 23, (3), pp. 609–612
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© 2015 The Institution of Engineering and Technology.
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John Wiley & Sons, Inc.
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Published: 01 February 2015
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