Lipreading Using Spatiotemporal Histogram of Oriented Gradients

We propose a visual speech parametrization based on histogram of oriented gradients (HOG) for the task of lipreading from frontal face videos. Inspired by the success of spatiotemporal local binary patterns, the features are designed to capture dynamic information contained in the input video sequence by combining HOG descriptors extracted from three orthogonal planes that span x, y and t axes. We integrate our features into a system based on hidden Markov model (HMM) and show that by utilizing robust and properly tuned parametrization this traditional scheme can outperform recent sophisticated embedding approaches to lipreading. We perform experiments on three different datasets, two of which are publicly available. In order to conduct an unbiased feature comparison, the process of model learning including hyperparameter tuning is as automatized as possible. To this end, we rely heavily on cross validation.