AFLFP: A Database With Annotated Facial Landmarks for Facial Palsy

Abstract

Facial landmark detection is a crucial step for the task of computer-aided facial palsy diagnosis, which enables to focus on the affected facial regions for learning asymmetry, shape, and texture features of facial palsy. However, it is still very challenging to accurately detect salient landmarks on facial palsy images due to the unavailability of sufficient training databases providing annotated facial landmark images of facial palsy. To this end, we present a database in this article named annotated facial landmarks for facial palsy (AFLFP). AFLFP is a diverse, and reliable database that contains facial images with 16-class facial expressions of asymmetric facial expressions from 88 subjects. Each facial image is independently and manually annotated with 68 facial landmarks. This database is the first public manually annotated facial landmark database for facial palsy so far. Furthermore, to establish the benchmark results for the proposed database, we propose a deep neural network (DNN) baseline with a two-stage cascaded fully convolutional network (FCN), which can detect facial landmarks in facial palsy from coarse to fine. The comprehensive experiments show that the proposed method performs better than the mainstream methods of machine-and deep-learning. And we have also compared the performance using normal-and palsy-faces, respectively, as the training data. The comparison results show that there are significant differences between them in terms of facial landmark detection, which further confirms the necessity to develop a facial landmark database specifically for facial palsy.