Safely caching HOG pyramid feature levels, to speed up facial landmark detection

This thesis presents an algorithm for improving the execution time of existing Histogram of Oriented Gradients (HOG) pyramid analysis based facial landmark detection. It extends the work of [1] to video data. A Bayesian Network (Bayes Net) is used as a policy network to determine when previously calculated features can be safely reused. This avoids the problem of recalculating expensive features every frame. The algorithm leverages a set of lightweight features to minimize additional overhead. Additionally, it takes advantage of the wide spread adoption of H.264 encoding in consumer grade recording devices, to acquire cheap motions vectors. Experimental results on a difficult real world data set show that policy network is effective, and that the error introduced to the system remains relatively low. A large performance benefit is realized due to the use of the cached features.