  In this paper, we propose a recursive framework to recognize facial
expressions from images in real scenes. Unlike traditional approaches that
typically focus on developing and refining algorithms for improving recognition
performance on an existing dataset, we integrate three important components in
a recursive manner: facial dataset generation, facial expression recognition
model building, and interactive interfaces for testing and new data collection.
To start with, we first create a candid-images-for-facial-expression (CIFE)
dataset. We then apply a convolutional neural network (CNN) to CIFE and build a
CNN model for web image expression classification. In order to increase the
expression recognition accuracy, we also fine-tune the CNN model and thus
obtain a better CNN facial expression recognition model. Based on the
fine-tuned CNN model, we design a facial expression game engine and collect a
new and more balanced dataset, GaMo. The images of this dataset are collected
from the different expressions our game users make when playing the game.
Finally, we evaluate the GaMo and CIFE datasets and show that our recursive
framework can help build a better facial expression model for dealing with real
scene facial expression tasks.
