Recognition of facial expressions - Knowledge Based Systems ...

CONCLUSION
The human face has attracted attention in the areas such as psychology, computer vision,
and computer graphics. Reading and understanding people expression have become one
of the most important research areas in recognition of human face. Many computer vision
researchers have been working on tracking and recognition of the whole or parts of face.
However, the problem of facial expression recognition has not totally been solved yet.
The current project addresses the aspects related to the development of an automatic
probabilistic recognition system for facial expressions in video streams.
The coding system used for encoding the complex facial expressions is inspired by
Ekman's Facial Action Coding System. The description of the facial expressions is given
according to sets of atomic Action Units (AU) from the Facial Action Coding System
(FACS). Emotions are complex phenomena that involve a number of related subsystems
and can be activated by any one (or by several) of them.
In order to make the data ready for the learning stage of the recognition system, a
complete set of software applications was developed. The initial image files from the
training database were processed for extracting the essential information in a
semiautomatic manner. Further the data were transformed so as to fit the requirements of
the learning stage for each kind of classifier.
The current project presents a fully automatic method, requiring no such human
specification. The system first robustly detects the pupils using an infrared sensitive
camera equipped with infrared LEDs. The face analysis component integrates an eye
tracking mechanism based on Kalman filter.
For each frame, the pupil positions are used to localize and normalize the other facial
regions. The visual feature detection includes PCA oriented recognition for ranking the
activity in certain facial areas.
The recognition system consists mainly of two stages: a training stage, where the
classifier function is learnt, and a testing stage, where the learnt classifier function
classifies new data.