Robust face detection using haar features with weber local descriptor and local binary pattern

Abstract

Face detection is the first step in many visual processing systems like face recognition, emotion recognition, gender recognition and etc. In this paper, we propose a novel method for fast and reliable face detection, especially in complex background. Firstly, the image is divided into small blocks to extract Local Binary Pattern (LBP) features and Simplified Weber Local Descriptor (SWLD) features. Secondly, these features compares values of Local Binary Pattern and Simplified Weber Local Descriptor calculated over two adjacent image subregions. These subregions are similar to those in the Haar masks. Thirdly, the HLBP and HSWLD features are combined together to ensure the classification capability of Haar features, an improvement on the Weber Local Descriptor (WLD) contains differential excitation component and Local Binary Pattern (LBP) component. These two components are complementary to each other. Specifically, differential excitation preserves the local intensity information but omits the orientations of edges. On the contrary, LBP describes the orientations of the edges but ignore the intensity information. Finally, the fused features are boosted using AdaBoost in a framework similar to that proposed by Viola and Jones. Experimental results show that our method outperforms the methods that are based on Haar features, SWLD features or LBP features individually.