FAST HAAR TRANSFORM BASED FEATURE EXTRACTION FOR MULTIMODAL BIOMETRIC SYSTEM ABSTRACT In many realworld applications, unimodal 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. Multibiometric 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 singlebiometric 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 highdimensional data onto the learned lowdimensional subspace. The projection operation requires many ﬂoatingpoint multiplications and additions, which makes the projection process computationally expensive. To tackle this problem, this project proposes two simplebuteffective 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
DET Detection Error Tradeoff
CCD Charge Coupled Display
JPEG Joint Photographic Expert Group
GIF Graphics Interchange Format
BMP Bit MaP
EPS Encapsulated Post Script
PNG Portable Netwoks Graphics
HDF Hierarchial Data Format
AVI Audio Video Interface
OOP Object Oriented Programming
TIFF Tagged Image File Format
1.INTRODUCTION
Software and computer systems are recognized as a subset of simulated intelligent behaviors of human beings described by programmed instructive information. According to Wang, computing methodologies and technologies are developed to extend human capability, reachability, persistency, memory, and information processing speed. Biometric information system is one of the ﬁnest examples of computer system that tries to imitate the decisions that humans make in their everyday life, speciﬁcally concerning people identiﬁcation and matching tasks. In this quest, the biometric systems evolved from simple singlefeaturebased models to a complex decisionmaking mechanism that utilize artiﬁcial intelligence, neural networks, complex decision making schemes, and multiple biometric parameters extracted and combined in an intelligent way. The main goal and contribution of this Project is to present a comprehensive analysis of various biometric fusion techniques in combination with advanced biometric feature extraction mechanisms that improve the performance of the biometric information system in the challenging and not resolved problem of people identiﬁcation. A biometric identiﬁcation (matching) system is an automatic pattern recognition system that recognizes a person by determining the authenticity of a speciﬁc physiological and/or behavioral characteristic (biometric) possessed by that person. Physiological biometric identiﬁers include ﬁngerprints, hand...
...and STFT 
ELEM018: Advanced Transform Methods 

Arsal Javid 
12/1/2011 
[Type the abstract of the document here. The abstract is typically a short summary of the contents of the document. Type the abstract of the document here. The abstract is typically a short summary of the contents of the document.] 
Section 2:
The following code was used to calculate perform the DFT Function in Matlab:
function sw = dft(st)
% DFT  Discrete Fourier...
...Information Processing Letters 75 (2000) 243–246
A fast algorithm for computing large Fibonacci numbers
Daisuke Takahashi
Department of Information and Computer Sciences, Saitama University, 255 ShimoOkubo, Urawashi, Saitama 3388570, Japan Received 13 March 2000; received in revised form 19 June 2000 Communicated by K. Iwama
Abstract We present a fast algorithm for computing large Fibonacci numbers. It is known that the product of Lucas numbers algorithm...
...Lecture 11 Fast Fourier Transform (FFT)
Weinan E1,2 and Tiejun Li2
1
Department of Mathematics,
Princeton University,
weinan@princeton.edu
2
School of Mathematical Sciences,
Peking University,
tieli@pku.edu.cn
No.1 Science Building, 1575
Examples
Fast Fourier Transform
Outline
Examples
Fast Fourier Transform
Applications
Applications
Examples
Fast Fourier Transform...
...The Discrete Cosine Transform
(DCT):
Theory and Application
1
Syed Ali Khayam
Department of Electrical & Computer Engineering
Michigan State University
March 10th 2003
1
This document is intended to be tutorial in nature. No prior knowledge of image processing concepts is
assumed. Interested readers should follow the references for advanced material on DCT.
ECE 802 – 602: Information Theory and Coding
Seminar 1 – The Discrete Cosine...
...Fractional Fourier Transform
Adhemar Bultheel and H´ctor E. Mart´ e ınez Sulbaran 1
Dept. of Computer Science, Celestijnenlaan 200A, B3001 Leuven
Abstract In this note we make a critical comparison of some matlab programs for the digital computation of the fractional Fourier transform that are freely available and we describe our own implementation that ﬁlters the best out of the existing ones. Two types of transforms are considered: First the...
...BEGE.104
Bachelor's l)egree Programme
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English for B usiness Communication
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Programme Code: BDp
Course Code: BEGE...
...2. Analysis of Signals
Figure 2.45.: Approximate FTs of two bandlimited signals
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The Hilbert transform of a function is by deﬁnition,
H {x(t)} = xh (t) =
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1
π
(2.171)
which is the convolution of x(t) with 1/π t,
H {x(t)} = xh (t) = x(t) ∗
1
πt
(2.172)
if we take the FT of this convolution,
Xh (ω ) = X (ω ) × F
1
πt
(2.173)
From Example 2.24,
F {sgn(t)} =
2
jω
(2.174)
and...
326 Words 
4 Pages
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