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Gabor Wavelets in Behavioral Biometrics

Gabor Wavelets in Behavioral Biometrics

M. Ashraful Amin, Hong Yan
Copyright: © 2010 |Pages: 30
ISBN13: 9781605667256|ISBN10: 1605667250|ISBN13 Softcover: 9781616924041|EISBN13: 9781605667263
DOI: 10.4018/978-1-60566-725-6.ch006
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MLA

Amin, M. Ashraful, and Hong Yan. "Gabor Wavelets in Behavioral Biometrics." Behavioral Biometrics for Human Identification: Intelligent Applications, edited by Liang Wang and Xin Geng, IGI Global, 2010, pp. 121-150. https://doi.org/10.4018/978-1-60566-725-6.ch006

APA

Amin, M. A. & Yan, H. (2010). Gabor Wavelets in Behavioral Biometrics. In L. Wang & X. Geng (Eds.), Behavioral Biometrics for Human Identification: Intelligent Applications (pp. 121-150). IGI Global. https://doi.org/10.4018/978-1-60566-725-6.ch006

Chicago

Amin, M. Ashraful, and Hong Yan. "Gabor Wavelets in Behavioral Biometrics." In Behavioral Biometrics for Human Identification: Intelligent Applications, edited by Liang Wang and Xin Geng, 121-150. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-725-6.ch006

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Abstract

The Gabor wavelets are employed regularly in various biometrics applications because of their biological relevance and computational properties. These wavelets have kernels similar to the 2D receptive field profiles of the mammalian cortical simple cells. They exhibit desirable characteristics of spatial locality and orientation selectivity, and are optimally localized in the space and frequency domains. Physiological, biometric systems such as face, fingerprint, and iris based human identification have shown great improvement in identification accuracies if Gabor wavelets are used for feature extraction. Moreover, some behavioral biometric systems such as speaker and gait based applications have shown more than 7% increase in identification accuracies. In this study, we provide a brief discussion on the origin of Gabor wavelets, then an illustration of “how to use Gabor wavelets” to extract features for a generic biometric application is discussed. We also provide an implementation pseudocode for the wavelet. It also offers an elaborate discussion on biometric applications with specific emphasis on behavioral biometric systems that have used Gabor wavelets. We also provide guideline for some biometric systems that have not yet applied Gabor wavelets for feature extraction.

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