One level deep convolutional neural network for facial key points detection

Abstract

Facial landmark detection has a lot of applications in face recognition, face alignment, facial expression recognition, video surveillance and security systems. In the existing literature, there are multiple methods utilizing convolutional neural networks (CNNs) that address this problem in various ways. In many cases, the models use a tree-like structure of CNNs to achieve better results. This paper proposes a combination of three parallel deep convolutional neural networks (DCNNs) to estimate the accurate localization of each keypoint. The first one focuses on the whole face to outperform five points, including the eyes, nose, and mouth corners. The second one focuses on the eyes-nose parts to outperform three points, specifically the eyes and nose. The last one focuses on the nose-mouth parts to outperform three points, namely the nose and mouth corners. Further, we combine all outputs of the three DCNNs and take the average value of each detected key point as the final output. In the first step, we improvthe the parameter efficiency and accuracy of each DCNNs through a set of experiments using the labeled face parts in-the-wild database (LFPW) and the helen facial feature dataset (Helen). Then, we demonstrate that our approach yields more accurate estimations of facial key points than two state-of-the-art methods and commercial software in terms of accuracy.