Abstract
In this paper, an improved classifier based on the concept of feature line space, called as adaptively nearest feature point classifier (ANFP) is proposed for face recognition. ANFP classifier uses the new metric, called as adaptively feature point metric, which is different from metrics of NFL and the other classifiers. ANFP gain better performance than NFL classifier and some others classifiers based on feature line space, which is proved by the experiment result on Yale face database.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991)
Belhumeur, P., Hespanha, J., Kriegman, D.: Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 711–720 (1997)
Bartlett, M.S., Movellan, J.R., Sejnowski, T.J.: Face recognition by independent component analysis. IEEE Transactions on Neural Networks 13(6), 1450–1464 (2002)
He, X., Yan, S., Hu, Y., Niyogi, P., Zhang, H.J.: Face recognition using laplacianfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(3), 1–13 (2005)
Kekre, H.B., Shah, K.: Performance Comparison of Kekre’s Transform with PCA and Other Conventional Orthogonal Transforms for Face Recognition. Journal of Information Hiding and Multimedia Signal Processing 3(3), 240–247 (2012)
Zhou, X., Nie, Z., Li, Y.: Statistical analysis of human facial expressions. Journal of Information Hiding and Multimedia Signal Processing 1(3), 241–260 (2010)
Cover, T.M., Hart, P.E.: Nearest neighbor pattern classification. IEEE Trans. Inform. Theory 13(1), 21–27 (1967)
Li, S.Z., Lu, J.: Face Recognition Using the Nearest Feature Line Method. IEEE Transactionson Neural Networks 10(2), 439–443 (1999)
Chien, J.T., Wu, C.C.: Discriminant waveletfaces and nearest feature classifiers for face recognition. IEEE Trans. Pattern Anal. Machine Intell. 24(12), 1644–1649 (2002)
Lu, J., Tan, Y.-P.: Uncorrelated discriminant nearest feature line analysis for face recognition. IEEE Signal Process. Lett. 17(2), 185–188 (2010)
Feng, Q., Pan, J.-S., Yan, L.: Restricted Nearest Feature Line with Ellipse for Face Recognition. Journal of Information Hiding and Multimedia Signal Processing 3(3), 297–305 (2012)
Feng, Q., Huang, C.-T., Yan, L.: Resprentation-based Nearest Feature Plane for Pattern Recognition. Journal of Information Hiding and Multimedia Signal Processing 4(3), 178–191 (2013)
Li, S.Z.: Content-based audio classification and retrieval using the nearest feature line method. IEEE Trans. Speech Audio Process. 8(5), 619–625 (2000)
Chen, K., Wu, T.Y., Zhang, H.J.: On the use of nearest feature line for speaker identification. Pattern Recognition Lett. 23(14), 1735–1746 (2002)
Li, S.Z., Chan, K.L., Wang, C.L.: Performance evaluation of the nearest feature line method in image classification and retrieval. IEEE Trans. Pattern Anal. Machine Intell. 22(11), 1335–1339 (2000)
Chen, J.H., Chen, C.S.: Object recognition based on image sequences by using inter-feature-line consistencies. Pattern Recognition 37(9), 1913–1923 (2004)
Zheng, W., Zhao, L., Zou, C.: Locally nearest neighbor classifiers for pattern classification. Pattern Recognition 37(6), 1307–1309 (2004)
Zhou, Y., Zhang, C., Wang, J.: Extended nearest feature line classifier. In: Zhang, C., Guesgen, H.W., Yeap, W.K. (eds.) PRICAI 2004. LNCS (LNAI), vol. 3157, pp. 183–190. Springer, Heidelberg (2004)
Zhou, Z., Kwoh, C.K.: The pattern classification based on the nearest feature midpoints. International Conference on Pattern Recognition 3, 446–449 (2000)
Han, D.Q., Han, C.Z., Yang, Y.: A Novel Classifier based on Shortest Feature Line Segment. Pattern Recognition Letters 32(3), 485–493 (2011)
Feng, Q., Pan, J.S., Yan, L.: Nearest feature centre classifier for face recognition. Electronics Letters 48(18), 1120–1122 (2012)
The Yale faces database (2001), http://cvc.yale.edu/projects/yalefaces/yalefaces.html
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Feng, Q., Pan, JS., Yan, L., Pan, TS. (2014). Adaptively Nearest Feature Point Classifier for Face Recognition. In: Abraham, A., Krömer, P., Snášel, V. (eds) Innovations in Bio-inspired Computing and Applications. Advances in Intelligent Systems and Computing, vol 237. Springer, Cham. https://doi.org/10.1007/978-3-319-01781-5_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-01781-5_11
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-01780-8
Online ISBN: 978-3-319-01781-5
eBook Packages: EngineeringEngineering (R0)