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Facial expression recognition, which many researchers have put much effort in, is an important portion of affective computing and artificial intelligence. However, human facial expressions change so subtly that recognition accuracy of most traditional approaches largely depend on feature extraction. Meanwhile, deep learning is a hot research topic in the field of machine learning recently, which intends...
Automatic facial expression analysis is an interesting and challenging problem which impacts important applications in many areas such as human-computer interaction and data-driven animation. Deriving effective facial representative features from face images is a vital step towards successful expression recognition. In this paper, we evaluate facial representation based on statistical local features...
Biologically Inspired Feature is one of efficient feature descriptions, and achieves great performance in some applications. This paper proposes an improved Biologically Inspired Feature(IBIF), and applies this feature into smile recognition. The main contributions of our paper are as follows. 1) a rotation-invariant BIF feature is proposed, which adjusts the RBF function of the traditional Biologically...
In this paper, a method based on local feature bidirectional two-dimensional principal component analysis is proposed for facial expression recognition. First of all, a facial expressional image is divided into three separate sub-blocks, from the horizontal and vertical directions we utilize bidirectional 2DPCA to extract local feature of each sub-block. To different parts of human face containing...
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