Multi-View Graph Fusion for Semi-Supervised Learning: Application to Image-Based Face Beauty Prediction

Facial Beauty Prediction (FBP) is an important visual recognition problem to evaluate the attractiveness of faces according to human perception. Most existing FBP methods are based on supervised solutions using geometric or deep features. Semi-supervised learning for FBP is an almost unexplored research area. In this work, we propose a graph-based semi-supervised method in which multiple graphs are constructed to find the appropriate graph representation of the face images (with and without scores). The proposed method combines both geometric and deep feature-based graphs to produce a high-level representation of face images instead of using a single face descriptor and also improves the discriminative ability of graph-based score propagation methods. In addition to the data graph, our proposed approach fuses an additional graph adaptively built on the predicted beauty values. Experimental results on the SCUTFBP-5500 facial beauty dataset demonstrate the superiority of the proposed algorithm compared to other state-of-the-art methods

Facial Beauty Prediction and Analysis based on Deep Convolutional Neural Network: A Review

Abstract: Facial attractiveness or facial beauty prediction (FBP) is a current study that has several potential usages. It is a key difficulty area in the computer vision domain because of the few public databases related to FBP and its experimental trials on the minor-scale database. Moreover, the evaluation of facial beauty is personalized in nature, with people having personalized favor of beauty. Deep learning techniques have displayed a significant ability in terms of analysis and feature representation. The previous studies focussed on scattered portions of facial beauty with fewer comparisons between diverse techniques. Thus, this article reviewed the recent research on computer prediction and analysis of face beauty based on deep convolution neural network DCNN. Furthermore, the provided possible lines of research and challenges in this article can help researchers in advancing the state – of- art in future work

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

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

Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective

This paper takes a problem-oriented perspective and presents a comprehensive review of transfer learning methods, both shallow and deep, for cross-dataset visual recognition. Specifically, it categorises the cross-dataset recognition into seventeen problems based on a set of carefully chosen data and label attributes. Such a problem-oriented taxonomy has allowed us to examine how different transfer learning approaches tackle each problem and how well each problem has been researched to date. The comprehensive problem-oriented review of the advances in transfer learning with respect to the problem has not only revealed the challenges in transfer learning for visual recognition, but also the problems (e.g. eight of the seventeen problems) that have been scarcely studied. This survey not only presents an up-to-date technical review for researchers, but also a systematic approach and a reference for a machine learning practitioner to categorise a real problem and to look up for a possible solution accordingly