3D/4D Face Recognition: a Comprehensive Survey
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)
Automatic face recognition has achieved great advances over the past two decades, with good performance achieved under certain const rained conditions. However, the solution is still challenged by variations in illumination, facial pose and expression. In this paper, we survey in the state of the art o f 3D and 4D face recognition. Then, 3D face recognition approaches, categorized into four main groups: point clounds-based approach, sub-space transform-based approach, local geometric features-based approach and model-based approach are reviewed, respectively. And the paper list the advantage, features, recognition algorithm and recognition performance of several typical 3D faces recognition methods. Finally, the paper summarizes the challenges existing in 3D face recognition and the future development trend
Copyright © 2013 Praise Worthy Prize - All rights reserved.
Georgia Sandbach, Stefanos Zafeiriou, Maja Pantic , Lijun Yin, Static and dynamic 3D facial expression recognition: A comprehensive survey, Image and Vision Computing,vol. 30 ,pp: 683–697,2012.
Xiaozheng Zhang, Yongsheng Gao, Face recognition across pose: A review, Pattern Recognition, Vol. 42, n. 11, pp. 2876-2896, 2006.
Shet, M.S., Patel, M., Manikantan, K., Ramachandran, S., DWT based feature extraction using Normalized Magnitude based Thresholding and Multilevel Illumination Normalization for enhanced Face Recognition, (2012) International Review on Computers and Software (IRECOS), 7 (5), pp. 1969-1985.
A.F. Abate, M. Nappi, D. Riccio, G. Sabatino, 2D and 3D face recognition: a survey, Pattern Recognition Letters , Vol. 28, n. 14, pp. 1885–19068, 2007.
R. Jafri, H.R. Arabnia, A survey of face recognition techniques, Information Processing Systems, Vol.5, n.2, pp. 41–688, 2009.
G. Medioni and R. Waupotitsch, Face Modeling and Recognition in 3-D, Proc. IEEE Int’l Workshop Analysis and Modeling of Faces and Gestures, pp. 232-233, Oct. 2003.
J. Cook, V. Chandran, S. Sridharan, and C. Fookes, Face Recognition from 3D Data Using Iterative Closest Point Algorithm and Gaussian Mixture Models, Proc. Second Int’l Symp. 3D Data Processing, Visualization, and Transmission, pp. 502-509, 2004.
Lu, X., Jain, A. K., & Colbry, D. Matching 2.5d face scans to 3d models. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28, n. 1, pp. 31–43,2006.
Kyong I. Chang, Kevin W. Bowyer, and Patrick J. Flynn, IEEE Multiple Nose Region Matching for 3D Face Recognition under Varying Facial Expression, IEEE Transactions on Pattern Analysis and Machine Intelligence,Vol. 28, n. 10, 2006.
K.W.Bowyer, K.Chang,P.Flynn, A survey of approaches and challenges in3d and multi-modal 3d+2d face recognition, Computer Vision and Image Understanding, Vol. 101, n. 1, pp. 1-15, 2006.
H.Mahoora, Mohamed Abdel-Mottalebb,Face recognition based on 3D ridge images obtained from range data Mohammad, Pattern Recognition,Vol.42,pp. 445-451 ,2009.
Luuk Spreeuwers, Fast and Accurate 3D Face Recognition Using Registration to an Intrinsic Coordinate System and Fusion of Multiple Region Classifiers, Int J Comput Vis, Vol. 93, pp. 389–414, 2011.
Ying Wen, Lianghua He, Pengfei Shi, Face recognition using difference vector plus KPCA, Digital Signal Processing, Vol. 22, pp. 140–146, 2012.
J. Jones, L. Palmer, An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex, Journal of Neurophysiology ,pp.1233–1258,1987.
L. Wiskott, J. Fellous, N. Kruger, C.V. Malsburg, Face recognition by elastic bunch graph matching, IEEE Transactions on Pattern Analysis and Machine Intelligence，Vol. 19, n. 7, pp. 775–779, 1997.
Chenghua Xu, StanLi, TieniuTan,, LongQuan, Automatic 3D face recognition from depth and intensity Gabor features ，Pattern Recognition ，Vol. 42, pp. 1895–1905, 2009.
Yingjie Wang, Chin-Seng Chua, Face recognition from 2D and 3D images using 3D Gabor filters ,Image and Vision Computing, Vol. 23, n. 11, pp. 1018-1028, 2005.
Claudio A. Perez, Leonardo A. Cament, Luis E. Castillo, Methodological improvement on local Gabor face recognition based on feature selection and enhanced Borda count , Pattern Recognition, Vol. 44, n. 4, pp. 951–963,2011.
Tie Yun, Ling Guan, Human emotional state recognition using real 3D visual features from Gabor library, Pattern Recognition, Vol. 46, n. 2, pp. 529–538, 2013.
S.M.S. Islam, R. Davies, M. Bennamoun, R.A. Owens, A.S. Mian, Multibiometric human recognition using 3D ear and face features, Pattern Recognition, Vol. 46, n. 3, pp. 613–627, 2013.
D.Shan,R.Ward,Wavelet-based illumination normalization for face recognition, in:Proceedings of the IEEE International Conference on Image Processing, vol.2,2005,pp.II-954-7.
Y.Z.Goh, AndrewB.J.Teoh, MichaelK.O.Goh, Waveletbased illumination invariant preprocessing in face recognition, in: Proceedings of the 2008 Congress on Image and Signal Processing, vol.3, 2008, pp. 421– 425.
T. P. Zhang, B.Fang, Y.Yuan, Y.Y.Tang, Z.W. Shang, D.H.Li,F.N. Lang, Multiscale facial structure representation for face recognition under varying illumination, Pattern Recognition, Vol. 42, pp. 251–258, 2009.
X. Cao a, W.Shen b, L.G.Yu a, Y.L.Wanga, J.Y.Yang a, Z.W.Zhang ,Illumination invariant extraction for face recognition using neighboring wavelet coefficients, Pattern Recognition, Vol. 45, pp. 1299–1305, 2012.
T. Mandal, Q.M.J. Wu, Y. Yuan, Curvelet based face recognition via dimension reduction, Elsevier Signal Processing , Vol. 89, n. 3, pp. 2345–2353, 2009.
G.C. Feng, P.C. Yuen, D.Q. Dai, Human face recognition using PCA on wavelet subband, Journal of Electronic Imaging, Vol. 9, n. 2, pp. 226–233, 2000.
J.T. Chien, C.C. Wu, Discriminant waveletfaces and nearest feature classifiers for face recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, n. 2, pp. 1644–1649, 2002.
B.L. Zhang, H. Zhang, S. Sam Ge, Face recognition by applying wavelet subband representation and kernel associative memory, IEEE Transactions on Neural Networks, Vol. 15, n. 1, pp. 166–177, 2004.
M. Zhao, P. Li, Z. Liu, Face recognition based on wavelet transform weighted modular PCA, Proceedings of the Congress in Image and Signal Processing (2008) 589–593.
A.A. Mohammed, R. Minhas, Q.M. Jonathan Wu, M.A. Sid-Ahmed, Human face recognition based on multidimensional PCA and extreme learning machine ,Pattern Recognition, Vol.44, pp. 2588–2597775–779, 2011.
Phillips, P. J., Flynn, P. J., Scruggs, T., Bowyer, K.W., Chang, J., Hoffman,mK., Marques, J., Min, J., & Worek, W.. Overview of the face recognition grand challenge. In Computer vision and pattern recognition, 2005. CVPR 2005. IEEE computer society conference on (Vol. 1, pp. 947–954).
Husken, M., Brauckmann, M., Gehlen, S., Von der Malsburg, C.,. Strategies and benefits of fusion of 2D and 3D face recognition, In: Proc. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition Workshops, 2005,pp. 174–174.
Chua, C., Han, F., Ho, Y., 2000. 3D human face recognition using point signature. In: Proc. IEEE Internat. Conf. on Automatic Face and Gesture Recognition, pp. 233–238.
Shalini Gupta, Mia K. Markey, Alan C. Bovik, Anthropometric 3D Face Recognition ,Int J Comput Vis, Vol. 90, pp. 331–349 ,2010.
Chang, K. I., Bowyer, K. W., & Flynn, P. J. An evaluation of multimodal 2d+3d face biometrics. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, n. 4, pp. 619–624,2005.
Heseltine, T. N. Pears, & Austin, J. Three-dimensional face recognition: a fishersurface approach. In A. Campilho, & M. Kamel (Eds.), LNCS: Vol. 3212. International conference on image analysis and recognition (pp. 684–691). 2004,Berlin/Heidelberg:Springer.
BenAbdelkader, C., & Griffin, P. A.. Comparing and combining depth and texture cues for face recognition. Image and Vision Computing, Vol. 23, n. 3, pp. 339–352,2005.
Russ, T. D., Koch, M.W., & Little, C. Q. A 2d range Hausdorff approach for 3d face recognition. In Computer vision and pattern recognition, 2005 IEEE computer society conference on (Vol. 3,pp. 169–176).
Frank B. ter Haar, Remco C. Veltkamp, A 3D face matching framework for facial curves, Graphical Models, Vol. 71, pp. 77–91, 2009.
A.B.Moreno a, A.Sa′nchez a,_, E.Frı′as-Martı′nez b, J.F.Ve′ lez a, Three-dimensional facial surface modeling applied to recognition, Engineering Applications of Artificial Intelligence, Vol. 22, pp. 1233–1244, 2009.
Xiaoli Li , Feipeng Da, Efficient 3D face recognition handling facial expression and hair occlusion, Image and Vision Computing , Vol. 30, pp. 668–679, 2012.
Yinjie Lei, Mohammed Bennamoun, AmarA. El-Sallam , An efficient 3D face recognition approach based on the fusion of novel local low-level features ,Pattern Recognition, Vol. 46, pp. 24–37, 2013.
Mian, A., Bennamoun, M., Owens, R.,. Keypoint detection and local feature matching for textured 3D face recognition. Internat. J. Comput. Vision, Vol. 79, n. 1, pp. 1–12, 2008.
Guangpeng Zhang, Yunhong Wang, Robust 3D face recognition based on resolution invariant features, Pattern Recognition Letters, Vol. 32, pp. 1009–1019, 2011.
I.A. Kakadiaris, G. Passalis, G. Toderici, M.N. Murtuza, Y. Lu, N. Karampatziakis, T. Theoharis, Three-dimensional face recognition in the presence of facial expressions: an annotated deformable model approach, IEEE Trans. Pattern Anal. Vol. 29, pp. 640–649, 2007.
Yue Ming, Qiuqi Ruan, Robust sparse bounding sphere for 3D face recognition Original Research Article, Image and Vision Computing, Vol. 30, n. 8, pp. 524–534, 2012.
Boris Efraty, Emil Bilgazyev, Shishir Shah, Ioannis A. Kakadiaris, Profile-based 3D-aided face recognition Original Research Article, Pattern Recognition, Vol. 45, n. 1, pp. 43–53, 2012.
Tianhong Fang, Xi Zhao, Omar Ocegueda, Shishir K. Shah, Ioannis A. Kakadiaris, 3D/4D facial expression analysis: An advanced annotated face model approach, Image and Vision Computing , Vol. 30, pp. 738–749, 2012.
- There are currently no refbacks.
Please send any question about this web site to firstname.lastname@example.org
Copyright © 2005-2021 Praise Worthy Prize