Abstract
Facial beauty plays an important role in many fields today, such as digital entertainment, facial beautification surgery and etc. However, the facial beauty prediction task has the challenges of insufficient training datasets, low performance of traditional methods, and rarely takes advantage of the feature learning of Convolutional Neural Networks. In this paper, a transfer learning based CNN method that integrates multiple channel features is utilized for Asian female facial beauty prediction tasks. Firstly, a Large-Scale Asian Female Beauty Dataset (LSAFBD) with a more reasonable distribution has been established. Secondly, in order to improve CNN’s self-learning ability of facial beauty prediction task, an effective CNN using a novel Softmax-MSE loss function and a double activation layer has been proposed. Then, a data augmentation method and transfer learning strategy were also utilized to mitigate the impact of insufficient data on proposed CNN performance. Finally, a multi-channel feature fusion method was explored to further optimize the proposed CNN model. Experimental results show that the proposed method is superior to traditional learning method combating the Asian female FBP task. Compared with other state-of-the-art CNN models, the proposed CNN model can improve the rank-1 recognition rate from 60.40% to 64.85%, and the pearson correlation coefficient from 0.8594 to 0.8829 on the LSAFBD and obtained 0.9200 regression prediction results on the SCUT dataset.
Highlights
As the saying goes, ‘‘Beauty lies in the eyes of the beholder’’, facial beauty is an abstract concept and each person’s definition of beauty is different
We proposed an effective Convolutional neural network (CNN) with a novel Softmax-MSE loss function and a double activation layer to improve CNN’s self-learning ability on facial beauty prediction task
We concluded that a larger-scale dataset can effectively improve the prediction performance, including traditional method and deep learning method for Facial Beauty Prediction (FBP)
Summary
As the saying goes, ‘‘Beauty lies in the eyes of the beholder’’, facial beauty is an abstract concept and each person’s definition of beauty is different. For FBP task, the most challenging mainly focuses on the establishment of high-quality, large-scale datasets; the classification of inter-class similarities and insufficient learning of deep facial features. Y. Zhai et al.: Asian Female FBP Using Deep Neural Networks via Transfer Learning and Multi-Channel Feature Fusion craft labeling cost of large standard facial beautiful database because of the shortage of sufficient data. The use of large-scale asian female facial beauty datasets with deep feature learning method has not been well investigated yet. (2) An effective CNN model combining a novel Softmax-MSE loss function and a double activation layer is proposed to improve CNN’s self-learning ability on an asian female facial beauty prediction task.
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