Multi-Feature Facial Complexion Classification Algorithms Based on CNN

Variations in facial complexion serve as a telltale sign of underlying health conditions. Precisely categorizing facial complexions poses a significant challenge due to the subtle distinctions in facial features. Three multi-feature facial complexion classification algorithms leveraging convolutional neural networks (CNNs) are proposed. They fuse, splice, or independently train the features extracted from distinct facial regions of interest (ROI), respectively. Innovative frameworks of the three algorithms can more effectively exploit facial features, improving the utilization rate of feature information and classification performance. We trained and validated the three algorithms on the dataset consisting of 721 facial images that we had collected and preprocessed. The comprehensive evaluation reveals that multi-feature fusion and splicing classification algorithms achieve accuracies of 95.98% and 93.76%, respectively. The optimal approach combining multi-feature CNN with machine learning algorithms attains a remarkable accuracy of 97.78%. Additionally, these experiments proved that the multidomain combination was crucial, and the arrangement of ROI features, including the nose, forehead, philtrum, and right and left cheek, was the optimal choice for classification. Furthermore, we employed the EfficientNet model for training on the face image as a whole, which achieves a classification accuracy of 89.37%. The difference in accuracy underscores the superiority and efficacy of multi-feature classification algorithms. The employment of multi-feature fusion algorithms in facial complexion classification holds substantial advantages, ushering in fresh research directions in the field of facial complexion classification and deep learning.