# Augmented EMTCNN: A Fast and Accurate Facial Landmark Detection Network +
## 1 Introduction  
Facial landmarks such as eyes, nose, and mouth are prominent feature points on the face, and diverse tasks such as face recognition, gaze detection, person tracking, emotion recognition, and virtual makeup have been performed based on facial landmarks [1; 2]. In particular, to meet the diverse demands of tasks such as real-time processing or rendering on mobile devices, fast and accurate face landmark extraction is essential [3]. So far, plenty of research has been done on extracting facial landmarks. In recent years, as convolutional neural networks (CNNs) have shown overwhelmingly strong performance in the field of image classification [4] and object detection [5], they have been investigated for applications in facial landmark extraction. Facial landmarks are popularly represented by 68 points, which cover facial contours, eyes, eyebrows, nose, and mouth [6; 7; 8]. In an effort to detect such facial landmark points accurately, adding more convolution layers has been attempted, as in Visual Geometry Group Network (VGGNet) [9; 10]. Even though this produces better results, it requires more computational resources and is therefore not appropriate for real-time processing.  
Various deep learning models have also been proposed for real-time extraction of facial landmarks [11; 12; 13]. Such models usually use fewer than 10 points for real-time facial landmark extraction. However, such few facial landmark points fail to represent landmark properties such as shape accurately. In the previous work, to extract a sufficient number of facial landmark points in realtime, we proposed an EMTCNN model by extending the original multi-task cascaded convolutional neural network (MTCNN) model [12] which extracts five facial landmark points in real time. By making the CNN layers of the MTCNN model deeper, the EMTCNN model could extract 68 facial landmark points in real time. Even though the accuracy of landmark detection can be improved further by making the output network (O-Net) of the EMTCNN model even more in-depth, the processing time would increase sharply owing to the increased number of parameters. Hence, in this study, we augment the EMTCNN model using two convolution techniques--dilated convolution [14; 15; 16] and CoordConv [17]--to improve the detection accuracy while maintaining the processing speed. The former makes it possible to extend the receptive field without increasing the number of parameters. The latter allows the feature map to reflect the spatial coordinate information of facial landmarks. In addition, to make our model more robust, we construct a face image dataset based on open face data and other face data collected manually and augment it by using image operations such as flipping and illumination to retain diversity of the subject race, age, and gender, face posture, and image shooting environment.  
Through various experiments, we demonstrate that our proposed model can improve detection accuracy at similar speeds. This paper is organized as follows: Section 2 introduces some related works and Section 3 describes how to extract the facial landmark points in real time. Experimental results for the proposed method are described in Section 4, and Section 5 concludes the paper.