An Integrative Approach to Face and Expression Recognition from 3D Scans
Chao Li · InTech eBooks · 2010
7.1 Discussion Experiment 1 was aimed at determining the level of performance of the Facial Expression Recognition Module, by itself. Using the leave-one-out cross validation approach, 30 different tests were carried out (Each using 29 x 2 neutral faces and 29 x 2 smiling faces for training). The average success rate in identifying the expressions of the face belonging to the subject not used for training, in each case, was 90.8% with LDA and 92.5% when SVM was used. This confirms the capability of this module to successfully sort these two types of faces (neutral vs. smiling). Both algorithms were applied on the six facial features obtained from the range images (mw, md, lc, ag, hr and hm). Using these features, the actual choice of algorithm used to separate neutral from smiling faces did not seem to be critical.