  In this paper, Deterministic Cellular Automata (DCA) based video shot
classification and retrieval is proposed. The deterministic 2D Cellular
automata model captures the human facial expressions, both spontaneous and
posed. The determinism stems from the fact that the facial muscle actions are
standardized by the encodings of Facial Action Coding System (FACS) and Action
Units (AUs). Based on these encodings, we generate the set of evolutionary
update rules of the DCA for each facial expression. We consider a
Person-Independent Facial Expression Space (PIFES) to analyze the facial
expressions based on Partitioned 2D-Cellular Automata which capture the
dynamics of facial expressions and classify the shots based on it. Target video
shot is retrieved by comparing the similar expression is obtained for the query
frame's face with respect to the key faces expressions in the database video.
Consecutive key face expressions in the database that are highly similar to the
query frame's face, then the key faces are used to generate the set of
retrieved video shots from the database. A concrete example of its application
which realizes an affective interaction between the computer and the user is
proposed. In the affective interaction, the computer can recognize the facial
expression of any given video shot. This interaction endows the computer with
certain ability to adapt to the user's feedback.
