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Automatic emotion recognition from facial expression is one of the most intensively researched topics in affective computing and human–computer interaction. However, it is well known that due to the lack of 3-D feature and dynamic analysis the functional aspect of affective computing is insufficient for natural interaction. In this paper, we present an automatic emotion recognition approach from video...
Automatic emotion recognition from facial expression is one of the most intensively researched topics in affective computing and human-computer interaction. However, due to the lack of 3D feature and dynamic analysis the functional aspect of affective computing is insufficient for natural interaction. This paper presents an automatic emotion recognition approach from video sequences based on a fiducial...
Computer recognition of human emotional states is an important component for efficient human-computer interaction. In this paper we explore an approach for recognition of human emotion from the visual information. We perform feature selection by using the two dimensions fractional Fourier transform. As a generalization of Fourier transform, the two dimensions fractional Fourier transform (2D-FrFT)...
In this paper, an image retrieval framework combining content-based and content-free methods is proposed, which employs both short-term relevance feedback (STRF) and long-term relevance feedback (LTRF) as the means of user interaction. The STRF refers to iterative query-specific model learning during a retrieval session, and the LTRF is the estimation of a user history model from the past retrieval...
Human beings recognize similarity in scene perception based on their available high-level knowledge about the low-level visual features, which is gradually accumulated throughout their entire lives. Once there is not enough knowledge they tend to rely on low-level visual content. Inspired by this observation, we proposed a new framework of relevance feedback for content-free image retrieval to tackle...
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