Asian female facial beauty prediction using deep neural networks via transfer learning and multi-channel feature fusion

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

Face beauty analysis via manifold based semi-supervised learning

Beauty has always played an important role in society, implicitly influencing the hu- man interactions of our daily lives and more significant aspects, such as the mate choice or job interviews. And now, with the progress made in deep learning and fea- ture extraction, automatic facial beauty analysis has become an emerging research topic too. However, the subjectivity of beauty still hinders the developement in this area, due to the cost of collecting reliable labeled data, since the beauty score of an individual has to be determined according to various raters. To address this problem, we study the performances of four different semi-supervised manifold based algorithms, which can take advantage of both labeled and unlabeled data in the training phase, and we use them in two different datasets: SCUT-FBP and M 2 B. The learning algorithms are Local and Global Consistency, Flexible Man- ifold Embedding and Kernel Flexible Manifold Embedding. There is an additional algorithm, which, unlike the rest of them, instead of performing classification, ob- tains a non-linear transformation of the data to make the classification easier. All of these algorithms were designed to work on discrete classes, but we perform regres- sion, where labels are real numbers. So the first step, in chapter 2, is to analyse how the algorithms can be adapted to regression and to hypothesize which problems we could be encountering in this process. Secondly, we empirically test them (chapter 3). The best results are obtained with KFME on both datasets, achieving a mean average error of 0.0104 (out of 1) and a Pearson correlation of 0.9782 on SCUT-FBP dataset. With respect to M 2 B dataset, a mean average error of 0.0697 and a Pear- son correlation of 0.7757 are achieved on eastern faces, while a mean average error of 0.0717 and a Pearson correlation of 0.7848 are achieved on western faces. This dissertation ends with a final chapter discussing the results and proposing new topics of study for future work