Abstract
Identification and authentication by face recognition mainly use global face features. However, the recognition accuracy rate is still not high enough. This research aims to develop a method to increase the efficiency of recognition using global-face feature and local-face feature with four parts: the left-eye, right-eye, nose and mouth. This method is based on geometrical techniques used to find location of eyes, nose and mouth from the frontal face image. We used 115 face images for learning and testing. Each-individual person’s images are divided into three difference images for training and two difference images for testing. The Two-Dimension Principle Component Analysis (2D-PCA) technique is used for feature extraction and the Support Vector Machine (SVM) method is used for face recognition. The results show that the recognition percentage is 97.83%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Tang, C.-Y., Lin, Y.-C., Liu, C.-T.: Comparison Between PCA and 2D-PCA for Face Recognition. In: 18th IPPR Conference on Computer Vision, Grapics and Image Processing (CVGIP 2005) 2005, pp. 766–773.
Radha, V., Pushpalatha, M.: Comparison of PCA Based and 2DPCA Based Face Recognition Systems. International Journal of Engineering Science and Technology 2 (12), 7177–7182 (2010).
Sani, M.M., K.A. Ishak, Samad, S.A.: Classification using Adaptive Multiscale Retinex and Support Vector Machine for Face Recognition System. Journal of Applied Sciences 10, 506–511 (2010).
Le, T.H., Bui, L.: Face Recognition Based on SVM and 2DPCA. International Journal of Signal Processing, Image Processing and Patten Recognition 4, No. 3, 85–94 (2011).
Bui, L., Tran, D., Huang, X., Chetty, G.: Face Gender Recognition on 2D Principle Component Analysis and Support Vector Machine. In: Fourth International Conference on Network and System Security 2010, pp. 579–582
Jiao, F., Gao, W., Chen, X., Cui, G., Shan, S.: A Face Recognition Method Based on Local Feature Analysys. In: The 5th Asian Conference on Computer Vision (ACCV2002), Melbourne, Australia 2002, pp. 1–5
Cheddad, A., Mohamad, D., Manaf, A.A.: Exploiting Voronoi diagram properties in face segmentation and feature extraction. Pattern Recognition 41, 3842–3859 (2008).
Yang, J., Zhang, D., Frangi, A.F., Yang, J.: Two-dimensional PCA: A new approach to appearance-based face representation and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(1), 131–137 (2004).
Nguyen, N., Liu, W., Venkatesh, S.: Random Subspace Two-Dimensional PCA for Face Recognition. In: Advances in Multimedia Information Processing (PCM 2007) 2007, pp. 655–664.
Corinna, C. and V. Vladimir, 1995. Support vector Network. Machine Learning, 20: 273–297.
Swain, M.J., Ballard, D.H.: Indexing Via Color Histograms. In: Third International Conference on Computer Vision (ICCV), Dec 4–7 1990, pp. 390–393
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2013 Springer Science+Business Media, LLC
About this paper
Cite this paper
Valuvanathorn, S., Nitsuwat, S., Huang, M.L. (2013). Multi-Feature Face Recognition Based on 2D-PCA and SVM. In: The Era of Interactive Media. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-3501-3_6
Download citation
DOI: https://doi.org/10.1007/978-1-4614-3501-3_6
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-3500-6
Online ISBN: 978-1-4614-3501-3
eBook Packages: Computer ScienceComputer Science (R0)