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.
Asian female facial beauty prediction using deep neural networks via transfer learning and multi-channel feature fusion / Y. Zhai, Y. Huang, Y. Xu, J. Gan, H. Cao, W. Deng, R. Donida Labati, V. Piuri, F. Scotti. - In: IEEE ACCESS. - ISSN 2169-3536. - 8(2020 Mar), pp. 56892-56907.
|Titolo:||Asian female facial beauty prediction using deep neural networks via transfer learning and multi-channel feature fusion|
|Parole Chiave:||Convolutional neural network (CNN); double activation layer; facial beauty prediction (FBP); feature fusion; softmax-MSE loss; transfer learning|
|Settore Scientifico Disciplinare:||Settore ING-INF/05 - Sistemi di Elaborazione delle Informazioni|
|Data di pubblicazione:||mar-2020|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1109/ACCESS.2020.2980248|
|Appare nelle tipologie:||01 - Articolo su periodico|