SCUT-FBP5500: A Diverse Benchmark Dataset for Multi-Paradigm Facial Beauty Prediction

Facial beauty prediction (FBP) is a significant visual recognition problem to make assessment of facial attractiveness that is consistent to human perception. To tackle this problem, various data-driven models, especially state-of-the-art deep learning techniques, were introduced, and benchmark dataset become one of the essential elements to achieve FBP. Previous works have formulated the recognition of facial beauty as a specific supervised learning problem of classification, regression or ranking, which indicates that FBP is intrinsically a computation problem with multiple paradigms. However, most of FBP benchmark datasets were built under specific computation constrains, which limits the performance and flexibility of the computational model trained on the dataset. In this paper, we argue that FBP is a multi-paradigm computation problem, and propose a new diverse benchmark dataset, called SCUT-FBP5500, to achieve multi-paradigm facial beauty prediction. The SCUT-FBP5500 dataset has totally 5500 frontal faces with diverse properties (male/female, Asian/Caucasian, ages) and diverse labels (face landmarks, beauty scores within [1,~5], beauty score distribution), which allows different computational models with different FBP paradigms, such as appearance-based/shape-based facial beauty classification/regression model for male/female of Asian/Caucasian. We evaluated the SCUT-FBP5500 dataset for FBP using different combinations of feature and predictor, and various deep learning methods. The results indicates the improvement of FBP and the potential applications based on the SCUT-FBP5500.Comment: 6 pages, 14 figures, conference pape

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

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