Robust facial expression recognition based on Local Monotonic Pattern (LMP)

Automatic facial expression recognition is a prominent and challenging research interest with usefulness in a variety of fields. It is playing increasingly important role in the fields of human computer interaction, data-driven animation etc. Success of most facial image analysis solutions depend on an effective facial feature representation. This paper presents one such new appearance-based facial feature, the Local Monotonic Pattern (LMP). LMP can extract robust facial feature from a face image that gives accurate and reliable recognition performance for expression recognition. The LMP operator applied on a pixel, finds the monotonic intensity transition of neighboring pixels at different radii. The micro patterns thus found is enhanced with spatial information by tiling the image and taking histogram of each tile. The final feature vector is a collation of these histograms. This feature vector is then employed to classify expressions with well known machine learning method: Support Vector Machine (SVM). Experimental results using Cohn-Kanade expression database show that the LMP descriptor yields improved recognition rate against other existing appearance-based feature descriptors.