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This paper presents a novel and efficient facial image representation based on local binary pattern (LBP) texture features. The face image is divided into several regions from which the LBP feature distributions are extracted and concatenated into an enhanced feature vector to be used as a face descriptor. The performance of the proposed method is assessed in the face recognition problem under different challenges. Other applications and several extensions are also discussed.

Abstract-Dynamic texture is an extension of texture to the temporal domain. Description and recognition of dynamic textures have attracted growing attention. In this paper, a novel approach for recognizing dynamic textures is proposed and its simplifications and extensions to facial image analysis are also considered. First, the textures are modeled with volume local binary patterns (VLBP), which are an extension of the LBP operator widely used in ordinary texture analysis, combining motion and appearance. To make the approach computationally simple and easy to extend, only the co-occurrences on three orthogonal planes (LBP-TOP) are then considered. A block-based method is also proposed to deal with specific dynamic events, such as facial expressions, in which local information and its spatial locations should also be taken into account. In experiments with two dynamic texture databases, DynTex and MIT, both the VLBP and LBP-TOP clearly outperformed the earlier approaches. The proposed block-based method was evaluated with the CohnKanade facial expression database with excellent results. Advantages of our approach include local processing, robustness to monotonic gray-scale changes and simple computation.

This paper presents a novel and efficient texture-based method for modeling the background and detecting moving objects from a video sequence. Each pixel is modeled as a group of adaptive local binary pattern histograms that are calculated over a circular region around the pixel. The approach provides us with many advantages compared to the state-of-the-art. Experimental results clearly justify our model. Index TermsMotion, texture, background subtraction, local binary pattern.