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.
Facial beauty prediction (FBP) aims to develop a machine that automatically makes facial attractiveness assessment. To a large extent, the perception of facial beauty for a human is involved with the attributes of facial appearance, which provides some significant visual cues for FBP. Deep convolution neural networks (CNNs) have shown its power for FBP, but convolution filters with fixed parameters cannot take full advantage of the facial attributes for FBP. To address this problem, we propose an Attribute-aware Convolutional Neural Network (AaNet) that modulates the filters of the main network, adaptively, using parameter generators that take beauty-related attributes as extra inputs. The parameter generators update the filters in the main network in two different manners: filter tuning or filter rebirth. However, AaNet takes attributes information as prior knowledge, that is ill-suited to those datasets merely with task-oriented labels. Therefore, imitating the design of AaNet, we further propose a Pseudo Attribute-aware Convolutional Neural Network (P-AaNet) that modulates filters conditioned on global context embeddings (pseudo attributes) of input faces learnt by a lightweight pseudo attribute distiller. Extensive ablation studies show that the AaNet and P-AaNet improve the performance of FBP when compared to conventional convolution and attention scheme, which validates the effectiveness of our method.
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