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Most previous work focuses on how to learn discriminating appearance features over all the face without considering the fact that each facial expression is physically composed of some relative action units (AU). However, the definition of AU is an ambiguous semantic description in Facial Action Coding System (FACS), so it makes accurate AU detection very difficult. In this paper, we adopt a scheme...
Most previous facial expression analysis works only focused on expression recognition. In this paper, we propose a novel framework of facial expression analysis based on the ranking model. Different from previous works, it not only can do facial expression recognition, but also can estimate the intensity of facial expression, which is very important to further understand human emotion. Although it...
In this paper, we propose a novel framework for video-based facial expression recognition, which can handle the data with various time resolution including a single frame. We first use the haar-like features to represent facial appearance, due to their simplicity and effectiveness. Then we perform K-Means clustering on the facial appearance features to explore the intrinsic temporal patterns of each...
In this paper, we propose a new approach of facial expression recognition. In order to capture the temporal characteristic of facial expressions, we design dynamic haar-like features to represent the facial images, and code them into binary patterns for the further analysis. Based on the encoded features, Adaboost is employed to learn the combination of optimal discriminant features to construct the...
It is well known that how to extract dynamical features is a key issue for video based face analysis. In this paper, we present a novel approach of facial action units (AU) and expression recognition based on coded dynamical features. In order to capture the dynamical characteristics of facial events, we design the dynamical haar-like features to represent the temporal variations of facial events...
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