MDFA: A Quantitative Framework for the Analysis of Multimodal Facial Esthetics

Abstract

In the era of big data, the problem of facial beauty prediction (FBP) has been addressed using a combination of deep learning and esthetics based on data and models. Most existing methods are based on 2-D unimodal information processing. Owing to the high cost of 3-D data acquisition equipment, studies on the use of multimodal features of 2-D and 3-D for esthetic evaluation are scarce. Moreover, most existing methods are based on self-built 3-D datasets, which are limited to practical application scenarios of 2-D facial images. This study proposed a label distribution-based multimodal facial esthetic analysis framework (LDMFE). The LDMFE performed facial esthetic evaluation by combining 2-D and 3-D information following the process used by the human brain to conduct the 3-D esthetic evaluation. FBP was performed by extracting facial depth structure information using a depth information extraction network, DIENet, which comprises a facial structure perception layer (FSP-Layer) and an attention decision block (AD-Block). Furthermore, to ensure a high degree of agreement between the predicted label distribution of the network and the true distribution, a simple and efficient distribution measurement loss function called ${\mathcal {L}}_{\text {WD}}$ was proposed. Compared with the label distribution-based FBP loss and the latest FBP loss, ${\mathcal {L}}_{\text {WD}}$ was more stable and effective. The performance of LDMFE was evaluated using three datasets. The experimental results demonstrate that the LDMFE exhibits state-of-the-art performance.