Lip reading using a dynamic feature of lip images and convolutional neural networks

In this paper, a lip-reading method using a novel dynamic feature of lip images is proposed. The dynamic feature of lip images is calculated as the first-order regression coefficients using a few neighboring frames (images). It constiutes a better representation of the time derivatives to the basic static image. The dynamic feature is processed by using convolution neural networks (CNNs), which are able to reduce the negative influence caused by shaking of the subject and face alignment blurring at the feature-extraction level. Its effectiveness has been confirmed by word-recognition experiments comparing the proposed method with the conventional static (original) image.