MATLAB Source Code for a Biometric Identification System Based on Iris Patterns
01 Jan 2003-
About: The article was published on 2003-01-01 and is currently open access. It has received 493 citations till now. The article focuses on the topics: Iris recognition & Source code.
TL;DR: Methods for learning dictionaries that are appropriate for the representation of given classes of signals and multisensor data are described and dimensionality reduction based on dictionary representation can be extended to address specific tasks such as data analy sis or classification.
Abstract: We describe methods for learning dictionaries that are appropriate for the representation of given classes of signals and multisensor data. We further show that dimensionality reduction based on dictionary representation can be extended to address specific tasks such as data analy sis or classification when the learning includes a class separability criteria in the objective function. The benefits of dictionary learning clearly show that a proper understanding of causes underlying the sensed world is key to task-specific representation of relevant information in high-dimensional data sets.
TL;DR: The feasibility of using the periocular region as a biometric trait is studied, including the effectiveness of incorporating the eyebrows, and use of side information (left or right) in matching.
Abstract: The term periocular refers to the facial region in the immediate vicinity of the eye. Acquisition of the periocular biometric is expected to require less subject cooperation while permitting a larger depth of field compared to traditional ocular biometric traits (viz., iris, retina, and sclera). In this work, we study the feasibility of using the periocular region as a biometric trait. Global and local information are extracted from the periocular region using texture and point operators resulting in a feature set for representing and matching this region. A number of aspects are studied in this work, including the 1) effectiveness of incorporating the eyebrows, 2) use of side information (left or right) in matching, 3) manual versus automatic segmentation schemes, 4) local versus global feature extraction schemes, 5) fusion of face and periocular biometrics, 6) use of the periocular biometric in partially occluded face images, 7) effect of disguising the eyebrows, 8) effect of pose variation and occlusion, 9) effect of masking the iris and eye region, and 10) effect of template aging on matching performance. Experimental results show a rank-one recognition accuracy of 87.32% using 1136 probe and 1136 gallery periocular images taken from 568 different subjects (2 images/subject) in the Face Recognition Grand Challenge (version 2.0) database with the fusion of three different matchers.
Cites methods from "MATLAB Source Code for a Biometric ..."
...A public domain iris detector based on the Hough transformation is used for localizing the iris ....
TL;DR: This paper proposes a unified framework based on random projections and sparse representations that can simultaneously address all three issues mentioned above in relation to iris biometrics, and includes enhancements to privacy and security by providing ways to create cancelable iris templates.
Abstract: Noncontact biometrics such as face and iris have additional benefits over contact-based biometrics such as fingerprint and hand geometry. However, three important challenges need to be addressed in a noncontact biometrics-based authentication system: ability to handle unconstrained acquisition, robust and accurate matching, and privacy enhancement without compromising security. In this paper, we propose a unified framework based on random projections and sparse representations, that can simultaneously address all three issues mentioned above in relation to iris biometrics. Our proposed quality measure can handle segmentation errors and a wide variety of possible artifacts during iris acquisition. We demonstrate how the proposed approach can be easily extended to handle alignment variations and recognition from iris videos, resulting in a robust and accurate system. The proposed approach includes enhancements to privacy and security by providing ways to create cancelable iris templates. Results on public data sets show significant benefits of the proposed approach.
TL;DR: In this paper, a discriminant correlation analysis (DCA) is proposed for feature fusion by maximizing the pairwise correlations across the two feature sets and eliminating the between-class correlations and restricting the correlations to be within the classes.
Abstract: Information fusion is a key step in multimodal biometric systems. The fusion of information can occur at different levels of a recognition system, i.e., at the feature level, matching-score level, or decision level. However, feature level fusion is believed to be more effective owing to the fact that a feature set contains richer information about the input biometric data than the matching score or the output decision of a classifier. The goal of feature fusion for recognition is to combine relevant information from two or more feature vectors into a single one with more discriminative power than any of the input feature vectors. In pattern recognition problems, we are also interested in separating the classes. In this paper, we present discriminant correlation analysis (DCA), a feature level fusion technique that incorporates the class associations into the correlation analysis of the feature sets. DCA performs an effective feature fusion by maximizing the pairwise correlations across the two feature sets and, at the same time, eliminating the between-class correlations and restricting the correlations to be within the classes. Our proposed method can be used in pattern recognition applications for fusing the features extracted from multiple modalities or combining different feature vectors extracted from a single modality. It is noteworthy that DCA is the first technique that considers class structure in feature fusion. Moreover, it has a very low computational complexity and it can be employed in real-time applications. Multiple sets of experiments performed on various biometric databases and using different feature extraction techniques, show the effectiveness of our proposed method, which outperforms other state-of-the-art approaches.
TL;DR: An efficient algorithm for iris recognition using phase-based image matching - an image matching technique using phase components in 2D discrete Fourier transforms (DFTs) of given images is presented.
Abstract: This paper presents an efficient algorithm for iris recognition using phase-based image matching - an image matching technique using phase components in 2D discrete Fourier transforms (DFTs) of given images. Experimental evaluation using the CASIA iris image databases (versions 1.0 and 2.0) and Iris challenge evaluation (ICE) 2005 database clearly demonstrates that the use of phase components of iris images makes it possible to achieve highly accurate iris recognition with a simple matching algorithm. This paper also discusses the major implementation issues of our algorithm. In order to reduce the size of iris data and to prevent the visibility of iris images, we introduce the idea of 2D Fourier phase code (FPC) for representing iris information. The 2D FPC is particularly useful for implementing compact iris recognition devices using state-of-the-art digital signal processing (DSP) technology.
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