Real-Time Facial Feature Extraction Scheme Using Cascaded Networks
Hyeonwoo Kim, Hyungjoon Kim, Eenjun Hwang · 2019
Facial landmarks such as eyes, nose, and mouth are the most prominent feature points on the face. So far, many works have been done for efficiently extracting such landmarks from facial images. Utilizing more feature points for landmark extraction usually requires more processing time, which has been an obstacle to real-time processing or video processing. On the contrary, utilizing a too small number of feature points cannot represent diverse landmark properties such as shape accurately. In this paper, we propose a deep learning based method for extracting popular 68 feature points for facial landmarks quickly and accurately. To do that, we first detect all the faces in the image by using a cascaded structure composed of relatively light Convolution Neural Networks(CNN). Then, we perform facial landmark extraction for each face, which reduces the processing time a lot. We performed several experiments to evaluate the performance of our method. We report some of the results.