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Magnitude and Phase of Discriminative Orthogonal Radial Moments for Face Recognition

Magnitude and Phase of Discriminative Orthogonal Radial Moments for Face Recognition

Neerja Mittal, Ekta Walia, Chandan Singh
Copyright: © 2014 |Pages: 30
ISBN13: 9781466660304|ISBN10: 1466660309|EISBN13: 9781466660311
DOI: 10.4018/978-1-4666-6030-4.ch007
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MLA

Mittal, Neerja, et al. "Magnitude and Phase of Discriminative Orthogonal Radial Moments for Face Recognition." Computer Vision and Image Processing in Intelligent Systems and Multimedia Technologies, edited by Muhammad Sarfraz, IGI Global, 2014, pp. 131-160. https://doi.org/10.4018/978-1-4666-6030-4.ch007

APA

Mittal, N., Walia, E., & Singh, C. (2014). Magnitude and Phase of Discriminative Orthogonal Radial Moments for Face Recognition. In M. Sarfraz (Ed.), Computer Vision and Image Processing in Intelligent Systems and Multimedia Technologies (pp. 131-160). IGI Global. https://doi.org/10.4018/978-1-4666-6030-4.ch007

Chicago

Mittal, Neerja, Ekta Walia, and Chandan Singh. "Magnitude and Phase of Discriminative Orthogonal Radial Moments for Face Recognition." In Computer Vision and Image Processing in Intelligent Systems and Multimedia Technologies, edited by Muhammad Sarfraz, 131-160. Hershey, PA: IGI Global, 2014. https://doi.org/10.4018/978-1-4666-6030-4.ch007

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Abstract

It is well known that the careful selection of a set of features, with higher discrimination competence, may increase recognition performance. In general, the magnitude coefficients of some selected orders of ZMs and PZMs have been used as invariant image features. The authors have used a statistical method to estimate the discrimination strength of all the coefficients of ZMs and PZMs. For classification, only the coefficients with estimated higher discrimination strength are selected and are used in the feature vector. The performance of these selected Discriminative ZMs (DZMs) and Discriminative PZMs (DPZMs) features are compared to that of their corresponding conventional approaches on YALE, ORL, and FERET databases against illumination, expression, scale, and pose variations. In this chapter, an extension to these DZMs and DPZMs is presented by exploring the use of phase information along with the magnitude coefficients of these approaches. As the phase coefficients are computed in parallel to the magnitude, no additional time is spent on their computation. Further, DZMs and DPZMs are also combined with PCA and FLD. It is observed from the exhaustive experimentation that with the inclusion of phase features the recognition rate is improved by 2-8%, at reduced dimensions and with less computational complexity, than that of using the successive ZMs and PZMs features.

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