**4.1. Dimensionality reduction using principal component analysis (PCA)**

The LCGS algorithm is used to extract the features of a face image, and the dimension of the feature matrix is usually very large, which makes it difficult to use the classifier to train and test. Therefore, we adopt a state-of-the-art method Principal Component Analysis (PCA) [11, 12] to reduce the dimension after feature extraction. For the implementation of PCA, we set the principal component contribution rate as 0.95.
