This paper proposes a novel local feature descriptor, local directional number pattern (LDN), for face analysis, i.e., face and expression recognition. LDN encodes the directional information of the face's textures (i.e., the texture's structure) in a compact way, producing a more discriminative code than current methods. We compute the structure of each micro-pattern with the aid of a compass mask that extracts directional information, and we encode such information using the prominent direction indices (directional numbers) and sign-which allows us to distinguish among similar structural patterns that have different intensity transitions. We divide the face into several regions, and extract the distribution of the LDN features from them. Then, we concatenate these features into a feature vector, and we use it as a face descriptor. We perform several experiments in which our descriptor performs consistently under illumination, noise, expression, and time lapse variations. Moreover, we test our descriptor with different masks to analyze its performance in different face analysis tasks.

In this paper, we propose a novel local feature descriptor, Local Sign Directional Pattern (LSDP), for face expression recognition. LSDP encodes the directional information of the face's textures—i.e., the texture's structure—in a compact way, producing a more discriminating code than other state-of-the-art methods. The structure of each micro-pattern is encoded by using its prominent directions and sign—which allows it to distinguish among similar structural patterns that have different intensity transitions. We divide the face into several regions, from which we extract the distributions of the LSDP features. These features are concatenated into a feature vector, and used as a face descriptor, and the expression recognition is obtained with the aid of Support Vector Machine classifiers.

Deriving an effective facial feature from original face images is a vital step for a successful automatic facial expression recognition. In this paper, we proposed a new feature descriptor, Local Principal Texture Pattern (LPTP), for expression recognition. We compute the LPTP feature, at each pixel, by extracting the principal directions of the local neighborhood, and we code the intensity differences on these directions. The mixture of direction and contrast information makes our descriptor robust against rotation and illumination changes. Consequently, we represent each expression as a distribution of LPTP codes. Our experiments demonstrate the superiority of the proposed feature, on two facial expression databases, over the existing methods.