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http://dx.doi.org/10.33778/kcsa.2019.19.2.091

Local Prominent Directional Pattern for Gender Recognition of Facial Photographs and Sketches  

Makhmudkhujaev, Farkhod (경희대학교/컴퓨터공학과)
Chae, Oksam (경희대학교/컴퓨터공학과)
Publication Information
Convergence Security Journal / v.19, no.2, 2019 , pp. 91-104 More about this Journal
Abstract
In this paper, we present a novel local descriptor, Local Prominent Directional Pattern (LPDP), to represent the description of facial images for gender recognition purpose. To achieve a clearly discriminative representation of local shape, presented method encodes a target pixel with the prominent directional variations in local structure from an analysis of statistics encompassed in the histogram of such directional variations. Use of the statistical information comes from the observation that a local neighboring region, having an edge going through it, demonstrate similar gradient directions, and hence, the prominent accumulations, accumulated from such gradient directions provide a solid base to represent the shape of that local structure. Unlike the sole use of gradient direction of a target pixel in existing methods, our coding scheme selects prominent edge directions accumulated from more samples (e.g., surrounding neighboring pixels), which, in turn, minimizes the effect of noise by suppressing the noisy accumulations of single or fewer samples. In this way, the presented encoding strategy provides the more discriminative shape of local structures while ensuring robustness to subtle changes such as local noise. We conduct extensive experiments on gender recognition datasets containing a wide range of challenges such as illumination, expression, age, and pose variations as well as sketch images, and observe the better performance of LPDP descriptor against existing local descriptors.
Keywords
Local Histogram; Prominent Directions; Feature Extraction; Sketch Images; Gender Recognition;
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