Local Directional Number Pattern for Face Analysis: Face and Expression Recognition.
Abstract of Local Directional Number Pattern for Face Analysis
This Local Directional Number Pattern for Face Analysis: Face and Expression Recognition
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 Local Directional Number Pattern for Face Analysis: Face and Expression Recognition paper we introduced a novel encoding scheme, LDN, that takes advantage of the structure of the face’s textures and that encodes it efficiently into a compact code. LDN uses directional information that is more stable against noise than intensity, to code the different patterns from the face’s textures. Additionally, we analyzed the use of two different compass masks (a derivative-Gaussian and Kirsch) to extract this directional information, and their performance on different applications. In general, LDN, implicitly, uses the sign information of the directional numbers which allows it to distinguish similar texture’s structures with different intensity transitions—e.g., from dark to bright and vice versa.
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