Automated facial expression recognition using local transitional pattern (LTP)

Abstract

Automatic facial expression recognition is a prominent and challenging research interest with usefulness in a variety of fields. It plays an 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 thesis presents a novel appearance-based facial feature, the Local Transitional Pattern (LTP). LTP can extract robust facial feature from a face image that gives accurate and reliable recognition performance for expression recognition. The LTP 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). Cohn-Kanade expression database is used to conduct experiments comparing LTP descriptor’s performance against other well known appearance based feature descriptors. It shows that LTP descriptor has higher accuracy than LBP and Gabor descriptors and it is also more robust against non monotonic illumination.