Abstract
In this paper, three baseline face recognition algorithms are evaluated on the CAS-PEAL-R1 face database which is publicly released from a large-scale Chinese face database: CAS-PEAL. The main objectives of the baseline evaluations are to 1) elementarily assess the difficulty of the database for face recognition algorithms, 2) provide an example evaluation protocol on the database, and 3) identify the strengths and weakness of some popular algorithms. Particular description of the datasets used in the evaluations and the underlying philosophy are given. The three baseline algorithms evaluated are Principle Components Analysis (PCA), a combined Principle Component Analysis and Linear Discriminant Analysis (PCA+LDA), and PCA+LDA algorithm based on Gabor features (G PCA+LDA). Four face image preprocessing methods are also tested to emphasize the influences of the preprocessing methods on the performances of face recognition algorithms.
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
Zhao, W., Chellappa, R., Rosenfeld, A., Phillips, P.J.: Face Recognition: A Literature Survey. Technical Report, CS-TR4167, University of Maryland, Revised, CS-TR4167R (2000)
Chellappa, R., Wilson, C., Sirohey, S.: Human and Machine Recognition of Faces: A Survey. Proc. IEEE 83(5), 705–740 (1995)
Phillips, P.J., Moon, H., Rizvi, S., Rauss, P.: The FERET Evaluation Methodology for Face-Recognition Algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(10), 1090–1104 (2000)
Phillips, P.J., Grother, P., Ross, J., Blackburn, D., Tabassi, E., Bone, M.: Face Recognition Vendor Test 2002: Evaluation Report (March 2003)
Zhou, D., Zhang, X., Liu, B., Gao, W.: Introduction of the JDL Large-scale Facial Database. In: The 4th Chinese Conference on Biometrics Recognition (Sinobiometrics 2003), pp. 118–121 (2003)
Beveridge, J.R., She, K., Draper, B.A., Givens, G.H.: A Nonparametric Statistical Comparison of Principal Component and Linear Discriminant Subspaces for Face Recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 535–542 (December 2001)
Martinez, A.M., Kak, A.C.: PCA versus LDA. IEEE Trans. Pattern Analysis and Machine Intelligence 23(2), 228–233 (2001)
Turk, M., Pentland, A.: Face Recognition Using Eigenfaces. In: IEEE Conference on Computer Vision and Pattern Recognition (1991)
Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Trans. Pattern Analysis and Machine Intelligence 19(7), 711–720 (1997)
Kruger, N., Malsburg, C., Wiskott, L., Fellous, J.M.: Face Recognition by Elastic Bunch Graph Matching. IEEE Trans. Pattern Analysis and Machine Intelligence 19(7), 775–779 (1997)
Liu, C., Wechsler, H.: Gabor Feature Based Classification Using the Enhanced Fisher Linear Dsicriminant Model for Face Recognition. IEEE Trans. Image Processing 11(4), 467–476 (2002)
Shan, S., Gao, W., Cao, B., Zhao, D.: Illumination Normalization for Robust Face Recognition against Varying Lighting Conditions. In: IEEE International Workshop on Analysis and Modeling of Faces and Gestures, Nice, France, pp. 157–164 (2003)
Shan, S., Gao, W., Qing, L.: Generating Canonical Form Face Images for Illumination Insensitive Face Recognition. In: The 4th Chinese Conference on Biometrics Recognition (Sinobiometrics 2003), pp. 29–35 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cao, B., Shan, S., Zhang, X., Gao, W. (2004). Baseline Evaluations on the CAS-PEAL-R1 Face Database. In: Li, S.Z., Lai, J., Tan, T., Feng, G., Wang, Y. (eds) Advances in Biometric Person Authentication. SINOBIOMETRICS 2004. Lecture Notes in Computer Science, vol 3338. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30548-4_42
Download citation
DOI: https://doi.org/10.1007/978-3-540-30548-4_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24029-7
Online ISBN: 978-3-540-30548-4
eBook Packages: Computer ScienceComputer Science (R0)