Abstract
In this paper, a novel facial expression recognition method based on sparse representation is proposed. Most contemporary facial expression recognition systems suffer from limited ability to handle image nuisances such as low resolution and noise. Especially for low intensity expression, most of the existing training methods have quite low recognition rates. Motivated by sparse representation, the problem can be solved by finding sparse coefficients of the test image by the whole training set. Deriving an effective facial representation from original face images is a vital step for successful facial expression recognition. We evaluate facial representation based on weighted local binary patterns, and Fisher separation criterion is used to calculate the weighs of patches. A multi-layer sparse representation framework is proposed for multi-intensity facial expression recognition, especially for low-intensity expressions and noisy expressions in reality, which is a critical problem but seldom addressed in the existing works. To this end, several experiments based on low-resolution and multi-intensity expressions are carried out. Promising results on publicly available databases demonstrate the potential of the proposed approach.
Highlights
Expression is a basic way to express human’s emotion, and it is an effective non-verbal communication method
To verify the performance of multi-layer sparse representation (MLSR), sparse representation (SR) and support vector machines (SVM) are used for comparing, which are presented in Figure 9 and Table 3
We select three images for each expression the same as in the Cohn-Kanade Dataset (CK)+ database
Summary
Expression is a basic way to express human’s emotion, and it is an effective non-verbal communication method. Automatic facial expression recognition (AFER) has become more and more important and plays an important role in computer vision. Facial expression recognition has wide application prospects in areas such as human-computer interface (HCI), image retrieval and psychological research. According to different facial expression features, existing facial expression analysis approaches can be categorized into static images-based methods and dynamic image sequences-based methods
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