  This paper discusses a novel method for Facial Expression Recognition System
which performs facial expression analysis in a near real time from a live web
cam feed. Primary objectives were to get results in a near real time with light
invariant, person independent and pose invariant way. The system is composed of
two different entities trainer and evaluator. Each frame of video feed is
passed through a series of steps including haar classifiers, skin detection,
feature extraction, feature points tracking, creating a learned Support Vector
Machine model to classify emotions to achieve a tradeoff between accuracy and
result rate. A processing time of 100-120 ms per 10 frames was achieved with
accuracy of around 60%. We measure our accuracy in terms of variety of
interaction and classification scenarios. We conclude by discussing relevance
of our work to human computer interaction and exploring further measures that
can be taken.
