Similarity features for facial event analysis

Each facial event will give rise to complex facial appearance variation. In this paper, we propose similarity features to describe the facial appearance for video-based facial event analysis. Inspired by the kernel features, for each sample, we compare it with the reference set with a similarity function, and we take the log-weighted summarization of the similarities as its similarity feature. Due to the distinctness of the apex images of facial events, we use their cluster-centers as the references. In order to capture the temporal dynamics, we use the K-means algorithm to divide the similarity features into several clusters in temporal domain, and each cluster is modeled by a Gaussian distribution. Based on the Gaussian models, we further map the similarity features into dynamic binary patterns to handle the issue of time resolution, which embed the time-warping operation implicitly. The haar-like descriptor is used to extract the visual features of facial appearance, and Adaboost is performed to learn the final classifiers. Extensive experiments carried on the Cohn-Kanade database show the promising performance of the proposed method.