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Appl. Sci. 2018, 8(5), 827; https://doi.org/10.3390/app8050827

An Efficient Multiscale Scheme Using Local Zernike Moments for Face Recognition

1
Department of Computer Engineering, Istanbul Technical University, 34469 Istanbul, Turkey
2
Department of Computer Engineering, MEF University, 34396 Istanbul, Turkey
*
Author to whom correspondence should be addressed.
Received: 3 April 2018 / Revised: 13 May 2018 / Accepted: 17 May 2018 / Published: 21 May 2018
(This article belongs to the Section Computer Science and Electrical Engineering)
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Abstract

In this study, we propose a face recognition scheme using local Zernike moments (LZM), which can be used for both identification and verification. In this scheme, local patches around the landmarks are extracted from the complex components obtained by LZM transformation. Then, phase magnitude histograms are constructed within these patches to create descriptors for face images. An image pyramid is utilized to extract features at multiple scales, and the descriptors are constructed for each image in this pyramid. We used three different public datasets to examine the performance of the proposed method:Face Recognition Technology (FERET), Labeled Faces in the Wild (LFW), and Surveillance Cameras Face (SCface). The results revealed that the proposed method is robust against variations such as illumination, facial expression, and pose. Aside from this, it can be used for low-resolution face images acquired in uncontrolled environments or in the infrared spectrum. Experimental results show that our method outperforms state-of-the-art methods on FERET and SCface datasets. View Full-Text
Keywords: face recognition; local Zernike moments; local descriptors; face identification; face verification face recognition; local Zernike moments; local descriptors; face identification; face verification
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Basaran, E.; Gökmen, M.; Kamasak, M.E. An Efficient Multiscale Scheme Using Local Zernike Moments for Face Recognition. Appl. Sci. 2018, 8, 827.

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