Abstract
Facial expression recognition methods based on convolutional neural networks have greatly improved the recognition accuracy. However, the feature information of face images has not been fully extracted in these methods, which are mainly due to insufficient features of facial expressions in some Key positions, such as eyes, nose, and mouth. Adjusting the weights of these key positions can improve the accuracy of facial expression recognition. To address this problem, we propose a differential weight-based multi-dimension and multi-scale HOG algorithm (MDS-HOG). It can extract various types of deep features, thus enriching facial expression feature information. First, facial expression data is preprocessed using ENM region acquisition. Then, on the basis of the original HOG algorithm, the diagonal gradient information of the image is added to obtain the multi-dimension HOG feature. Furthermore, a multi-scale spatial pyramid is constructed to extract multi-scale HOG features. Finally, according to the different contributions of different face regions, a differential weight EMN algorithm is proposed to deal with the weight distribution of face sub-regions. Experimental results demonstrate that the proposed algorithm exhibits superior performance compared with the original and other improved HOG algorithms on JAFFE and CK+ datasets.
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04 April 2024
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s10878-024-01152-8
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This work was supported by the Project of the Huaibei Mining Group Intelligent Property Management System and Key Research and Development Projects in Anhui Province (202004b11020029).
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Sun, K., He, M., Zhang, D. et al. RETRACTED ARTICLE: Expression recognition algorithm based on MDS-HOG feature optimization and differential weights. J Comb Optim 45, 20 (2023). https://doi.org/10.1007/s10878-022-00935-1
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DOI: https://doi.org/10.1007/s10878-022-00935-1