Abstract
The recognition rate of person-independent facial expression is generally not high, which limits the practical application of facial expression recognition. Aiming at this problem, this paper analyzes the reasons for the low recognition rate of person-independent facial expression, and proposes a recognition algorithm of person-independent facial expression based on improved LBP (Local Binary Pattern) and HOSVD (Higher-Order Singular Value Decomposition). The algorithm has the following contributions of facial expression recognition framework. In the stage of facial expression feature extraction, the transient features extracted by LDP(Local Directional Pattern) and the persistent features extracted by CBP(Centralized Binary Pattern) are integrated to improve the discrimination of facial expression features. Moreover, in the stage of facial expression classification and recognition, the traditional nearest neighbor classification is changed into k-nearest neighbor pre-classification, and the regional energy calculated by HOSVD is used to determine the similarity of two images for secondary classification. Finally, in the extended Cohn-Kanade dataset and Oulu-CASIA NIR&VIS facial expression database, the theoretical analysis and experimental results show that the method has better recognition effect for solving the problem of person-independent facial expression recognition.
Highlights
With the rapid development of artificial intelligence, facial expression recognition has become one of the research hotspots
We find that the single use of texture feature extraction algorithm cannot extract expression features very effectively
The whole face, eyes, eyebrows and mouth regions are segmented by Viola Jones algorithm [13].Secondly the local persistent expression features are obtained by CBP (Centralized Binary Pattern), and the whole transient facial expression features are obtained by LDP (Local Directional Pattern)
Summary
With the rapid development of artificial intelligence, facial expression recognition has become one of the research hotspots. For person-independent facial expression recognition algorithm, Wang [8] and Sun [9] proposed to decompose the facial expression image by High-Order Tensor Model, and extract the expression features irrelevant to the face appearance, so as to improve the recognition rate. The whole face, eyes, eyebrows and mouth regions are segmented by Viola Jones algorithm [13].Secondly the local persistent expression features are obtained by CBP (Centralized Binary Pattern), and the whole transient facial expression features are obtained by LDP (Local Directional Pattern). These two features are fused into facial expression features for recognition. K facial expression image can be accurately recognized and the closest classification of facial expression image can be obtained
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