  Computing environment is moving towards human-centered designs instead of
computer centered designs and human's tend to communicate wealth of information
through affective states or expressions. Traditional Human Computer Interaction
(HCI) based systems ignores bulk of information communicated through those
affective states and just caters for user's intentional input. Generally, for
evaluating and benchmarking different facial expression analysis algorithms,
standardized databases are needed to enable a meaningful comparison. In the
absence of comparative tests on such standardized databases it is difficult to
find relative strengths and weaknesses of different facial expression
recognition algorithms. In this article we present a novel video database for
Children's Spontaneous facial Expressions (LIRIS-CSE). Proposed video database
contains six basic spontaneous facial expressions shown by 12 ethnically
diverse children between the ages of 6 and 12 years with mean age of 7.3 years.
To the best of our knowledge, this database is first of its kind as it records
and shows spontaneous facial expressions of children. Previously there were few
database of children expressions and all of them show posed or exaggerated
expressions which are different from spontaneous or natural expressions. Thus,
this database will be a milestone for human behavior researchers. This database
will be a excellent resource for vision community for benchmarking and
comparing results. In this article, we have also proposed framework for
automatic expression recognition based on convolutional neural network (CNN)
architecture with transfer learning approach. Proposed architecture achieved
average classification accuracy of 75% on our proposed database i.e. LIRIS-CSE.
