In many real-world applications, uni-modal biometric systems often face signiﬁcant limitations due to sensitivity to noise, interclass variability, data quality, non universality, and other factors. Attempting to improve the performance of individual matchers in such situations may not prove to be highly effective. Multi-biometric systems seek to alleviate some of these problems by providing multiple pieces of evidence of the same identity. These systems help achieve an increase in performance that may not be possible using a single-biometric indicator.
In this project we use multimodal biometric fast recognition method. Subspace learning is the process of ﬁnding a proper feature subspace and then projecting high-dimensional data onto the learned low-dimensional subspace. The projection operation requires many ﬂoating-point multiplications and additions, which makes the projection process computationally expensive. To tackle this problem, this project proposes two simple-but-effective fast subspace learning and image projection methods, fast Haar transform (FHT) based principal component analysis. The advantages of this methods result from employing both the FHT for subspace learning and the integral vector for feature extraction. Experimental results on face,iris and fingerprint databases demonstrated their effectiveness and efficiency.
LIST OF ABBREVIATIONS
FHT Fast Haar Transform
PCA Principal Component Analysis
FLD Fisher’s Linear Discriminant
DSP Digital Signal Processing
RGB Red Green Blue
FAR False Accept Rate
FRR False Reject Rate
FTE Failure To Enroll rate
GAR Genuine Accept Rate
EER Equal Error Rate
Bibliography:  P.N. Belhumeur, J.P. Hespanha, and D.J. Kriegman, “Eigenfaces vs. Fisherfaces: Recognition using class specification linear projection,” IEEE Trans. Pattern anal. Mach. Intell., vol.19,no. 7, pp. 711-720, Jul. 1997.  D. Cai, X. He, and J. Han, “SRDA: An efficient algorithm for largescale discriminant analysis,” IEEE Trans. Knowl. Data Eng., vol. 20, no. 1, pp. 1-12, Jan. 2008.  P. Getreur, Writing Fast Matlab Code 2008 [Online]. Available:WWW. Eng.auburn.edu/~dmbevly/matlab/  X. He, D. Cai, and J. Han, “Learning a maximum margin subspace for image retrieval,” IEEE Trans. Knowl. Data Eng., vol. 20, no.2, pp. 189-201, Feb.2008.  X. He,S. Yan, Y. Hu,P.Niyogi,and H. Zhang, “Face recognition using Laplacian faces, “ IEEE Trans. Pattern Anal. Mach.Intell., vol. 27, no.3, pp. 328-340, Mar. 2005.