DeepLip: block-based lip pixel detection by deep neural networks

Abstract

ABSTRACTThis paper presents an effective lip pixel detection method based on blocks and deep neural networks. Since only-rough localization of a pair of lips is a trivial task, we use a rectangle that loosely bounds two lips as an input region of interest for lip detection. For each pixel in the rectangle region we generate a block whose center is at the pixel, and the pixel is classified into either a lip or non-lip pixel by exploiting the pixels in the block. Deep neural networks are trained using a sufficient number of labeled blocks obtained from a quite tractable number of labeled images. As a result, lip pixels are detected with high accuracy despite negligible labeling effort. Experimental results demonstrate the effectiveness of the presented method. We show that even single-minute training can outperform the mouth map with the best threshold.