A dynamic inverse distance weighting-based local face descriptor
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A dynamic inverse distance weighting-based local face descriptor Nazife Cevik 1 Received: 24 May 2019 / Revised: 4 August 2020 / Accepted: 11 August 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
This paper proposes a novel high-performance dynamic inverse distance weighting based local descriptor (DIDWLD) for facial recognition. Studies proposed thus far have focused on finding local descriptors that can represent the texture of the face best. However, the robustness of the descriptors against rotational variances and noise affects have been largely omitted. Thus, this study does not only concern with proposing a highdiscriminative descriptor, but also a robust one against rotational changes and noise affects. DIDWLD mainly basis on Inverse Distance Weighting (IDW). That is, for each pixel in the image, a new descriptive value is calculated, taking into account the intensity values of the neighboring pixels and their distance to the reference pixels. A dynamic distance-decay parameter is applied throughout the image rather than keeping it uniform as done in ordinary IDW. The calculated descriptor is independent of the changes in the rotation. Because, when calculating the descriptor, the intensity values of the surrounding pixels with their distances to the reference pixel are taken into consideration, yet their directional relation to the reference pixel is ignored. Furthermore, when a pixel is suffered to noise, inherently, its neighboring pixels are also affected. Hence, by taking into account the effect of the surrounding pixels and also the original intensity value of the pixel, the degrading impact of noise on recognition performance is mitigated. The results of extensive simulations show the remarkable and competitive performance of the proposed method regarding recognition accuracy, and robustness against rotational variances and, noise effects. Keywords Facial recognition . Local descriptor . Inverse distance weighting . Classification . Rotation invariant
* Nazife Cevik [email protected]
1
Department of Computer Engineering, Istanbul Arel University, Istanbul, Turkey
Multimedia Tools and Applications
1 Introduction Individuals pose biological characteristics, which distinguish them from others. Biometrics deals with these features, and has attracted great attention in recent years due to its high discrimination performance in many areas such as surveillance, identification and humancomputer interaction [5, 23, 51]. These characteristics are generally classified under two headings as physiological and behavioral [22]. Voice, typing rhythm, gait are examples of the physiological characteristics that contain information about the behavioral characteristics of people [41]. Face is one of the most important characteristics used to distinguish people from others [67]. One of the most important reasons why the face is preferred is that face data can be collected remotely through devices such as cameras without the need for any human intervention [21]. Performing
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