Abstract
The human face is one of the most viewed visual objects in a person's life and is used for identifying a person through facial landmarks, which includes the eyes, nose, mouth, and ears that make up a face. It is also possible to communicate nonverbally through the movements of facial landmarks; that is, change of facial expression. Thus, facial landmarks play a crucial role in human-related image analysis. Automatic facial landmark detection is a challenging problem in the field of computer vision, and various studies are underway. The emergence of Deep Neural Networks has played an important role in solving difficult problems in computer vision. Semantic segmentation is a field in which images are classified into pixel units and has also developed rapidly by incorporating deep learning. In this paper, we propose a method for accurately extracting facial landmarks using semantic segmentation. First, we introduce a semantic segmentation architecture for sophisticated landmark detection, and datasets composed of facial images and ground truth pairs. Then, we suggest how improve the performance of pixel classification by adjusting the imbalance of the number of pixels according to the face landmark. Through extensive experiments, we evaluated our approach using the metrics pixel accuracy and intersection over union.
Highlights
One of the most common visual objects that people encounter in their lifetimes is human faces
Deep Neural Networks (DNNs) have been used in facial landmark detection research, and detection performance has significantly improved with their use [3]
EXPERIMENTS In the previous section, we described our pixel annotated facial landmark dataset and three methods that we used to build our FSLNet for improving semantic segmentation accuracy, which are class weights, shallow network and weighted feature map
Summary
One of the most common visual objects that people encounter in their lifetimes is human faces. People can identify individuals using facial landmarks: the features that make up a face, such as eyes, nose, and mouth. Humans express their feelings using facial expressions by moving their facial landmarks and can understand the emotions of other people by reading their facial expressions. Recognizing facial landmarks plays an essential role in identifying a person or analyzing a person’s emotions. Identifying facial landmarks in images of people is essential for human-related image analysis. Various studies into the detection of facial landmarks have been carried out, but the task is still challenging because of variations in face images caused by factors such as pose, lighting, and occlusions [1]
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