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Existing popular descriptors for facial expression recognition often suffer from inconsistent feature description, experiencing poor accuracies. We present a new local descriptor, local directional-structural pattern (LDSP), in this work to address this issue. Unlike the existing local descriptors using only the texture or edge information to represent the local structure of a pixel, the proposed LDSP utilizes the positional relationship of the top edge responses of the target pixel to extract more detailed structural information of the local texture. We further exploit such information to characterize expression-affiliated crucial textures while discarding the random noisy patterns. Moreover, we introduce a globally adaptive thresholding strategy to exclude futile flat patterns. Hence, LDSP offers a stable description of facial expressions with the explicit representation of the expression-affiliated features along with the exclusion of random futile textures. We visualize the efficacy of the proposed method in three folds. First, the LDSP descriptor possesses a moderate code-length owing to the exclusion of the futile patterns, yielding less computation time than other edge descriptors. Second, for person-independent expression recognition in benchmark datasets, LDSP demonstrates higher accuracy than existing descriptors and other state-of-the-art methods. Third, LDSP shows better performance than other descriptors against noise and low resolution, exhibiting its robustness under such uneven conditions.

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