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Emotion Detection using Deep Belief Networks

In this paper, we explore the exciting new field of deep learning. Recent discoveries have made it possible to efficiently train artificial neural networks with multiple layers of hidden units, allowing for more hierarchical, incrementally abstracted representations. We study the performance of a Deep Belief Network, a powerful deep learning architecture, on the task of classifying the emotion of face images. We find that the DBN architecture autonomously learns representationally useful and humaninterpretable features of the input, and that this method is still effective when pretrained with unlabeled data. Finally, we propose future extensions to refine our understanding of the many design choices involved in building a deep learning model.