College of Science, China Agricultural University, Beijing 100083, China.
Comput Intell Neurosci. 2022 Aug 29;2022:9521329. doi: 10.1155/2022/9521329. eCollection 2022.
With the development of artificial intelligence, facial expression recognition has become an important part of the current research due to its wide application potential. However, the qualities of the face features will directly affect the accuracy of the model. Based on the KDEF face public dataset, the author conducts a comprehensive analysis of the effect of linear discriminant analysis (LDA) dimensionality reduction on facial expression recognition. First, the features of face images are extracted respectively by manual method and deep learning method, which constitute 35-dimensional artificial features, 128-dimensional deep features, and the hybrid features. Second, LDA is used to reduce the dimensionality of the three feature sets. Then, machine learning models, such as Naive Bayes and decision tree, are used to analyze the results of facial expression recognition before and after LDA feature dimensionality reduction. Finally, the effects of several classical feature reduction methods on the effectiveness of facial expression recognition are evaluated. The results show that after the LDA feature dimensionality reduction being used, the facial expression recognition based on these three feature sets is improved to a certain extent, which indicates the good effect of LDA in reducing feature redundancy.
随着人工智能的发展，由于其广泛的应用潜力，面部表情识别已成为当前研究的重要组成部分。然而，面部特征的质量将直接影响模型的准确性。作者基于 KDEF 人脸公共数据集，对面部表情识别中线性判别分析（LDA）降维的效果进行了全面分析。首先，分别采用人工方法和深度学习方法提取人脸图像的特征，构成 35 维人工特征、128 维深度特征和混合特征。其次，使用 LDA 降低这三个特征集的维度。然后，使用机器学习模型（如朴素贝叶斯和决策树）分析 LDA 降维前后的面部表情识别结果。最后，评估了几种经典特征降维方法对面部表情识别有效性的影响。结果表明，在使用 LDA 降维后，基于这三个特征集的面部表情识别得到了一定程度的提高，这表明 LDA 在减少特征冗余方面具有良好的效果。
