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In this paper, we propose a recursive framework to recognize facialexpressions from images in real scenes. Unlike traditional approaches thattypically focus on developing and refining algorithms for improving recognitionperformance on an existing dataset, we integrate three important components ina recursive manner: facial dataset generation, facial expression recognitionmodel 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 aCNN model for web image expression classification. In order to increase theexpression recognition accuracy, we also fine-tune the CNN model and thusobtain a better CNN facial expression recognition model. Based on thefine-tuned CNN model, we design a facial expression game engine and collect anew and more balanced dataset, GaMo. The images of this dataset are collectedfrom the different expressions our game users make when playing the game.Finally, we evaluate the GaMo and CIFE datasets and show that our recursiveframework can help build a better facial expression model for dealing with realscene facial expression tasks.

A Recursive Framework for Expression Recognition: From Web Images to Deep Models to Game Dataset