We got a paper accepted on the IEEE Trans. on Image Processing 🙂 . It is about a new micro pattern descriptor that relies on directional numbers (direction indexes in a local neighborhood), and we tested it on face analysis tasks, namely, face identification and expression recognition. We’ve been working on these ideas for some time, and sadly the revision was delayed some time, but finally it’s over.
Abstract—This paper proposes a novel local feature descriptor, Local Directional Number Pattern (LDN), for face analysis: 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 indexes (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.