Approximate structured output learning for Constrained Local Models with application to real-time facial feature detection and tracking on low-power devices

Given a face detection, facial feature detection involves localizing the facial landmarks such as eyes, nose, mouth. Within this paper we examine the learning of the appearance model in Constrained Local Models (CLM) technique. We have two contributions: firstly we examine an approximate method for doing structured learning, which jointly learns all the appearances of the landmarks. Even though this method has no guarantee of optimality we find it performs better than training the appearance models independently. This also allows for efficiently online learning of a particular instance of a face. Secondly we use a binary approximation of our learnt model that when combined with binary features, leads to efficient inference at runtime using bitwise AND operations. We quantify the generalization performance of our approximate SO-CLM, by training the model parameters on a single dataset, and testing on a total of five unseen benchmarks. The speed at runtime is demonstrated on the ipad2 platform. Our results clearly show that our proposed system runs in real-time, yet still performs at state-of-the-art levels of accuracy.