49,621 research outputs found

    Learned Spatio-Temporal Texture Descriptors for RGB-D Human Action Recognition

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    Due to the recent arrival of Kinect, action recognition with depth images has attracted researchers' wide attentions and various descriptors have been proposed, where Local Binary Patterns (LBP) texture descriptors possess the properties of appearance invariance. However, the LBP and its variants are most artificially-designed, demanding engineers' strong prior knowledge and not discriminative enough for recognition tasks. To this end, this paper develops compact spatio-temporal texture descriptors, i.e. 3D-compact LBP (3D-CLBP) and local depth patterns (3D-CLDP), for color and depth videos in the light of compact binary face descriptor learning in face recognition. Extensive experiments performed on three standard datasets, 3D Online Action, MSR Action Pairs and MSR Daily Activity 3D, demonstrate that our method is superior to most comparative methods in respects of performance and can capture spatial-temporal texture cues in videos

    Objective Classes for Micro-Facial Expression Recognition

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    Micro-expressions are brief spontaneous facial expressions that appear on a face when a person conceals an emotion, making them different to normal facial expressions in subtlety and duration. Currently, emotion classes within the CASME II dataset are based on Action Units and self-reports, creating conflicts during machine learning training. We will show that classifying expressions using Action Units, instead of predicted emotion, removes the potential bias of human reporting. The proposed classes are tested using LBP-TOP, HOOF and HOG 3D feature descriptors. The experiments are evaluated on two benchmark FACS coded datasets: CASME II and SAMM. The best result achieves 86.35\% accuracy when classifying the proposed 5 classes on CASME II using HOG 3D, outperforming the result of the state-of-the-art 5-class emotional-based classification in CASME II. Results indicate that classification based on Action Units provides an objective method to improve micro-expression recognition.Comment: 11 pages, 4 figures and 5 tables. This paper will be submitted for journal revie
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