Abstract
With the development of integration and innovation of Internet and industry, facial expression recognition (FER) technology is widely applied in wireless communication and mobile edge computing. The sparse representation‐based classification is a hot topic in computer vision and pattern recognition. It is one type of commonly used image classification algorithms for FER in recent years. To improve the accuracy of FER system, this study proposed a sparse representation classifier embedding subspace mapping and support vector (SRC‐SM‐SV). Based on the traditional sparse representation model, SRC‐SM‐SV maps the training samples into a subspace and extracts rich and discriminative features by using the structural information and label information of the training samples. SRC‐SM‐SV integrates the support vector machine to enhance the classification performance of sparse representation coding. The solution of SRC‐SM‐SV uses an alternate iteration method, which makes the optimization process of the algorithm simple and efficient. Experiments on JAFFE and CK+ datasets prove the effectiveness of SRC‐SM‐SV in FER.
Highlights
At present, many countries are actively developing intelligent technologies focusing on mobile edge computing [1]
As one of the most important tasks in the field of emotional computing, facial expression recognition (FER) has wide applied in many practical applications, such as computer vision, multimedia entertainment, and machine intelligence
To improve the recognition performance of FER technology in practical applications, this paper explores and studies the FER system based on sparse representation classification
Summary
Many countries are actively developing intelligent technologies focusing on mobile edge computing [1]. Lee [7] established shape model and texture model for training samples at the same time in the model establishment stage and combined them to form active appearance models to obtain reliable expression features It is a geometric feature extraction method. In order to reduce the computation scale, we consider projecting the original image into a low-dimensional subspace and embedding a multiclass support vector machine into the sparse representation classification algorithm Based on this idea, this paper proposes a sparse representation classifying embedding subspace mapping and support vector machine (SRC-SM-SV). In detail, when learning sparse coding, the proposed algorithm uses the Laplacian regularization term and principal component analysis to mine the geometric structure information of sample features in a low-dimensional subspace. A series of experiments on Japanese female facial expression database (JAFFE) [22] and extended Cohn Kanade (CK+) [23] datasets are carried out to compare with the proposed algorithm, which further proves the effectiveness of the proposed algorithm
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