Asymmetric and symmetric gradient operators with application in face recognition in Renaissance portrait art

Artyom M. Grigoryan; Sos S. Agaian

doi:10.1117/12.2518364

30 December 2019 Asymmetric and symmetric gradient operators with application in face recognition in Renaissance portrait art

Artyom M. Grigoryan, Sos S. Agaian

Author Affiliations +

Proceedings Volume 10993, Mobile Multimedia/Image Processing, Security, and Applications 2019; 109930J (2019) https://doi.org/10.1117/12.2518364
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

Abstract

Automatic face recognition research includes a wide range of commercial and law enforcement applications. However, only a few works focus on face recognition in the Renaissance portrait artworks which is essential to characterize the individual artists. The primary challenge of the portrait recognition is the availability of limited portraits. To cope with those issues, we develop a new class of new gradient operators for face recognition in renaissance portrait art. In mathematics, the gradient is an extension of the derivative. Gradient operators have been extensively used in many image processing and computer vision applications. The simplest examples are the Roberts, Prewitt and Sobel operators, the circular operator, the rotationally symmetric operator, and the isotropic operator. In this paper, we propose a class of gradient asymmetric and symmetric a small size (3×3 and 5×5) operators. Different examples of generated 5×5 gradient operators in the different directions are described. Extensive computer simulation is directed on 270 Renaissance portraits, including Raphael, Michelangelo, and Leonardo Da Vinci portraits. The experimental results show that the fusion of local binary patterns (LBP) and asymmetric and symmetric operators are better than traditional LBP features for face recognition, including in Renaissance portraits.

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Artyom M. Grigoryan and Sos S. Agaian "Asymmetric and symmetric gradient operators with application in face recognition in Renaissance portrait art", Proc. SPIE 10993, Mobile Multimedia/Image Processing, Security, and Applications 2019, 109930J (30 December 2019); https://doi.org/10.1117/12.2518364

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