Deep Learning For Smile Recognition
This work addresses the problem of accurate facial expression recognition for applications in human-computer interaction, though it is incremental as it adapts existing deep learning methods to a specific domain.
The paper tackled smile recognition by applying deep convolutional neural networks to the DISFA database, achieving a test accuracy of 99.45%, which significantly outperformed existing hand-crafted feature methods with accuracies between 65.55% and 79.67%.
Inspired by recent successes of deep learning in computer vision, we propose a novel application of deep convolutional neural networks to facial expression recognition, in particular smile recognition. A smile recognition test accuracy of 99.45% is achieved for the Denver Intensity of Spontaneous Facial Action (DISFA) database, significantly outperforming existing approaches based on hand-crafted features with accuracies ranging from 65.55% to 79.67%. The novelty of this approach includes a comprehensive model selection of the architecture parameters, allowing to find an appropriate architecture for each expression such as smile. This is feasible because all experiments were run on a Tesla K40c GPU, allowing a speedup of factor 10 over traditional computations on a CPU.