Facial Beauty Prediction Fusing Transfer Learning and Broad Learning System

Abstract

Facial Beauty Prediction (FBP) is an important and challenging problem in the field of computer vision and machine learning. Not only it is easily prone to over-fitting due to the lack of large-scale and effective data, but also difficult to quickly build robust and effective face beauty evaluation models because of the variability of facial appearance and the complexity of human perception. Transfer learning can be able to reduce the dependence on large amounts of data as well as avoid overfitting problems. Broad Learning System (BLS) can be capable of quickly completing models building and training. For this purpose, transfer learning was fused with BLS for facial beauty prediction in this paper. Firstly, a feature extractor is constructed by way of CNN model based on transfer learning for facial feature extraction, in which EfficientNet is used in this paper, and the facial features extracted are transferred to BLS for facial beauty prediction, called E-BLS. Then, on the basis of E-BLS, a connection layer is designed to connect the feature extractor and BLS, called ER-BLS. Experimental results show that, compared with the previous BLS and CNN methods existed, the accuracy of facial beauty prediction was improved by E-BLS and ER-BLS, indicating the effectiveness of the method presented, which can also be widely used in pattern recognition, object detection and image classification, etc.