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.

Date Published: 30 December 2019
PDF: 12 pages
Proc. SPIE 10993, Mobile Multimedia/Image Processing, Security, and Applications 2019, 109930J (30 December 2019); doi: 10.1117/12.2518364

Published in SPIE Proceedings Vol. 10993:
Mobile Multimedia/Image Processing, Security, and Applications 2019
Sos S. Agaian; Vijayan K. Asari; Stephen P. DelMarco, Editor(s)