Abstract
Microexpression is usually characterized by short duration and small action range, and the existing general expression recognition algorithms do not work well for microexpression. As a feature extraction method, non-negative matrix factorization can decompose the original data into different components, which has been successfully applied to facial recognition. In this paper, local non-negative matrix factorization is explored to decompose microexpression into some facial muscle actions, and extract features for recognition based on apex frame. However, the existing microexpression datasets fall short of samples to train a classifier with good generalization. The macro-to-micro algorithm based on singular value decomposition can augment the number of microexpressions, but it cannot meet non-negative properties of feature vectors. To address these problems, we propose an improved macro-to-micro algorithm to augment microexpression samples by manipulating the macroexpression data based on local non-negative matrix factorization. Finally, several experiments are conducted to verify the effectiveness of the proposed scheme, which results show that it has a higher recognition accuracy for microexpression compared with the related algorithms based on CK+/CASME2/SAMM datasets.
Highlights
Expression is one of the important ways for human to communicate emotion
The algorithm evaluation compares the performance of the proposed MtM algorithm with original MtM/main directional mean optical flow (MDMO)/tensor-independent color space (TICS) on CASME2, and SA-AT/ATNet/OFF-ApexNet on SAMM
It shows that macroexpression features extracted by negative matrix factorization (NMF) are suitable for MtM transformation to augment microexpression samples
Summary
Expression is one of the important ways for human to communicate emotion. In 1970s, American psychologist Paul Ekman defined six basic expressions of human, namely, happiness, anger, surprise, fear, disgust, and sadness. In 2002, Ekman developed microexpression training tool (METT) (Ekman, 2006) that can train the recognition skills of the six basic emotions and other kinds of expressions, such as contempt, pain, and so on. Microexpression is weak, short term, and difficult to detect, so the traditional expression recognition algorithms do not work well at all for this task. The former is to determine whether there are microexpressions in an image sequences, and detect the start/apex/end frames of a microexpression The latter includes feature extraction and classification, which is similar to the general tasks of pattern classification. The related algorithms can be used for extracting features, which can reflect the microexpression action information and distinguish various kinds of emotions. The feature classification is to train a classifier on the obtained vectors, directly related to the recognition accuracy, to distinguish the types of microexpression.
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