Weighted Gradient Feature Extraction Based on Multiscale Sub-Blocks for 3D Facial Recognition in Bimodal Images

Abstract

In this paper, we propose a bimodal 3D facial recognition method aimed at increasing the recognition rate and reducing the effect of illumination, pose, expression, ages, and occlusion on facial recognition. There are two features extracted from the multiscale sub-blocks in both the 3D mode depth map and 2D mode intensity map, which are the local gradient pattern (LGP) feature and the weighted histogram of gradient orientation (WHGO) feature. LGP and WHGO features are cascaded to form the 3D facial feature vector LGP-WHGO, and are further trained and identified by the support vector machine (SVM). Experiments on the CASIA database, FRGC v2.0 database, and Bosphorus database show that, the proposed method can efficiently extract the structure information and texture information of the facial image, and have a robustness to illumination, expression, occlusion and pose.

Abnormalities in Ultrasonic (C-Scan) Images of Composite Structures: Impact Damaged Versus Hole Damaged

“…The algorithm is based on multilevel thresholding of RGB images resulting from C-Scan testing with correlative displacement of gradient fields associated with damaged areas of composite structures. Using the proposed algorithm an extraction of damage visualization from a C-Scan image is carried out (Atta and Abdel-Kader, 2015;Xu et al, 2015;Singh et al, 2015;Guo et al, 2018;Lee et al, 2017;Furnari et al, 2017;Huang et al, 2015;Iskandarani, 2018).…”