Abstract
The network of a face recognition system is very complex and therefore difficult to train. In order to reduce the complexity, a new face recognition algorithm based on average gray scale was proposed in this paper. Discussed are the data structures of the face feature vector and the average gray scale vector for face recognition. The paper also shows the readers the advantage of the new algorithm, Experiments have been conducted on ORL face database, the results show that the feature vector could be described easily by the gray-scale, the vector can be extracted in short time, also the neural network could decrease the training time, and this new method has higher recognition rate than Eigenface Algorithm in the same experiment conditions according to our practices.
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
Preview
Unable to display preview. Download preview PDF.
References
Moon, H., Phillips, P.J.: Computational and Performance aspects of PCA-based Face Recognition Algorithms. Perception 30, 303–321 (2001)
Chen, X., Gu, L., Li, S.Z., Zhang, H.-J.: Learning representative local features for face detection. In: 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 1126–1131 (2001)
van der Schaar, M., Chen, Y., Radha, H.: Embedded DCT and Wavelet Methods for Scalable Video: Analysis and Comparison. Visual Communications and Image Processing (January 2000)
Yang, Y., Xuping, Z.: General Theory Research on Morphological Correlation for Gray2Scale Face Recognition. Acta Photonica Sinica 35(2), 299–303 (2006)
Ryu, H., Yoon, J.-C., Chun, S.S., Sull, S.: Coarse-to-Fine Classification for Image-Based Face Detection. In: Sundaram, H., Naphade, M., Smith, J.R., Rui, Y. (eds.) CIVR 2006. LNCS, vol. 4071, pp. 291–299. Springer, Heidelberg (2006)
Hu, N.-p., Tian, J.-x.: Evaluation of the growth of real estate financial system based on BP neural network. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds.) ISNN 2007, Part III. LNCS, vol. 4493, pp. 49–56. Springer, Heidelberg (2007)
Lin, S.-H.: An Introduction to Face Recognition Technology. Information Science Special Issue on Multimedia informing Technologies=Part 2 3(1), 1–7 (2000)
Hu, Y., Yin, B., Cheng, S., Gu, C., Liu, W.: Research on key technology in construction of a chinese 3d face database. Journal of Computer Research and Development 42(4), 622–628 (2005)
zhao, L., Liu, J., Xu, X.: A Survey of Human Face Detection. Application Research of Computers 21(9), 1–4 (2004)
Buciu, I.: Non-negative Matrix Factorization, A New Tool for Feature Extraction: Theory and Applications. Int. J. of Computers, Communications & Control (III) (2008); ISSN 1841-9836, E-ISSN 1841-9844, Suppl. issue: Proceedings of ICCCC 2008, pp. 67–74 (2008)
Berry, M.W., Browne, M., Langville, A.N., Pauca, P.V., Plemmons, R.J.: Algorithms and applications for approximate nonnegative matrix factorization. Computational Statistics & Data Analysis 52(1), 155–173 (2007)
Liang, D., Yang, J., Chang, Y.: Relevance feedback based on non-negative matrix factorisation for image retrieval. IEE Proceedings-Vision Image and Signal Processing 153(4), 436–444 (2006)
Shlens, J.: A Tutorial on Principal Component Analysis. Version 2
Frigo, M., Johnson, S.G.: The Design and Implementation of FFTW3. Proceedings of the IEEE 93(2), 216–231 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Wang, W. (2010). Research on Face Recognition Technology Based on Average Gray Scale. In: Luo, Q. (eds) Advancing Computing, Communication, Control and Management. Lecture Notes in Electrical Engineering, vol 56. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05173-9_21
Download citation
DOI: https://doi.org/10.1007/978-3-642-05173-9_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-05172-2
Online ISBN: 978-3-642-05173-9
eBook Packages: Computer ScienceComputer Science (R0)