Using sparse coding for landmark localization in facial expressions

Abstract

In this article we address the issue of adopting a local sparse coding representation (Histogram of Sparse Codes), in a part-based framework for inferring the locations of facial landmarks. The rationale behind this approach is that unsupervised learning of sparse code dictionaries from face data can be an effective approach to cope with such a challenging problem. Results obtained on the CMU Multi-PIE Face dataset are presented providing support for this approach.