An improved scheme of local directional pattern for texture analysis with an application to facial expressions

Abstract

In this paper, several extensions and modifications of Local Directional Pattern (LDP) are proposed with an objective to increase its robustness and discriminative power. Typically, Local Directional pattern generates a code based on the edge response value for the eight directions around a particular pixel. This method ignores the center value which can include important information. LDP uses absolute value and ignores sign of the response which carries information about image gradient and may contain more discriminative information. The sign of the original value carries information about the different trends (positive or negative) of the gradient and may contain some more data. Centered Local Directional Pattern (CLDP), Signed Local Directional Pattern (SLDP) and Centered-SLDP (CSLDP) are proposed in different conditions. Experimental results on 20 texture types using 5 different classifiers in different conditions shows that CLDP in both upper and lower traversal and CSLDP substantially outperforms the formal LDP. All the proposed methods were applied to facial expression emotion application. Experimental results show that SLDP and CLDP outperform original LDP in facial expression analysis.