  Facial expressions convey nonverbal cues which play an important role in
interpersonal relations, and are widely used in behavior interpretation of
emotions, cognitive science, and social interactions. In this paper we analyze
different ways of representing geometric feature and present a fully automatic
facial expression recognition (FER) system using salient geometric features. In
geometric feature-based FER approach, the first important step is to initialize
and track dense set of facial points as the expression evolves over time in
consecutive frames. In the proposed system, facial points are initialized using
elastic bunch graph matching (EBGM) algorithm and tracking is performed using
Kanade-Lucas-Tomaci (KLT) tracker. We extract geometric features from point,
line and triangle composed of tracking results of facial points. The most
discriminative line and triangle features are extracted using feature selective
multi-class AdaBoost with the help of extreme learning machine (ELM)
classification. Finally the geometric features for FER are extracted from the
boosted line, and triangles composed of facial points. The recognition accuracy
using features from point, line and triangle are analyzed independently. The
performance of the proposed FER system is evaluated on three different data
sets: namely CK+, MMI and MUG facial expression data sets.
