Summary
Major problem in real world scenarios is lack of suficient image samples per person in database for successful face recognition. In most cases insuficient number of samples per an individual in database is present. This makes face classification almost impossible for larger number of people. This problem is commonly described as ’one sample problem’. Recent state-of-art in face recognition allows to achieve high accuracy using face images with frontal pose. However, recognizing faces with rotations in depth, increases error rate significantly. In this paper we present a method to expand database using 3D morphable models to reconstruct 3D face from a single frontal image sample. By rotating reconstructed face to different views we create series of novel virtual images with pose variations for every individual in database. This approach can help to decrease error rate from pose variations and resolves ’one sample problem’.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Beymer, D., Poggio, T.: Face Recognition From One Example View, A.I. Memo No. 1536, C.B.C.L. Paper No. 121 (September 1995)
Jiang, D., Hu, Y., Yan, S., Zhang, L., Zhang, H., Gao, W.: Efficient 3D Reconstruction for Face Recognition, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, China (2005)
Huang, J., Yuen, P.C., Chen, W., Lai, J.H.: Component-based LDA Method for Face Recognition with One Training Sample. In: Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures, AMFG (2003)
Unsang, P., Yiying, T., Anil, J.K.: Face Recognition with Temporal Invariance: A 3D Aging Model. Michigan State University, East Lansing (2008)
Portions of the research in this paper use the FERET database of facial images collected under the FERET program, sponsored by the DOD Counterdrug Technology Development Program Office and citations to: a. Phillips, P.J., Wechsler, H., Huang, J., Rauss, P.: The FERET database and evaluation procedure for face recognition algorithms. Image and Vision Computing J 16(5), 295–306 (1998); b. Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The FERET Evaluation Methodology for Face Recognition Algorithms. IEEE Trans. Pattern Analysis and Machine Intelligence 22, 1090–1104 (2000)
Xiaoyang, T., Songcan, C., Zhi-Hua, Z., Fuyan, Z.: Face Recognition from a Single Image per Person: A Survey, Departament of Computer Science and Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing, China (2006)
Bai, X., Yin, B., Shi, Q., Sun, Y.: Face Recognition Using Extended Fisherface With 3D Morphable Model. In: Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, August 18-21 (2005)
Zhang, X., Gao, Y., Leung, M.K.H.: Automatic Texture Synthesis for Face Recognition from Single Views. In: 18th International Conference on Pattern Recognition (2006)
Xiaozheng, Z., Yongsheng, G., Bai-ling, Z.: Recognizing Rotated Faces from Two Orthogonal Views in Mugshot Databases. In: Proceedings of the 18th International Conference on Pattern Recognition (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kulasek, Ł., Czyżewski, A. (2010). 3D Morphable Models Application for Expanding Face Database Limited to Single Frontal Face Image Per Person. In: Choraś, R.S. (eds) Image Processing and Communications Challenges 2. Advances in Intelligent and Soft Computing, vol 84. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16295-4_17
Download citation
DOI: https://doi.org/10.1007/978-3-642-16295-4_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16294-7
Online ISBN: 978-3-642-16295-4
eBook Packages: EngineeringEngineering (R0)