Fast Haar Transform

Topics: Biometrics, Image processing, Facial recognition system Pages: 45 (13040 words) Published: July 5, 2011
FAST HAAR TRANSFORM BASED FEATURE EXTRACTION FOR MULTIMODAL BIOMETRIC SYSTEM ABSTRACT 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.

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
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

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 single-feature-based models to a complex decision-making 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...
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