  This thesis describes the design and implementation of a smile detector based
on deep convolutional neural networks. It starts with a summary of neural
networks, the difficulties of training them and new training methods, such as
Restricted Boltzmann Machines or autoencoders. It then provides a literature
review of convolutional neural networks and recurrent neural networks. In order
to select databases for smile recognition, comprehensive statistics of
databases popular in the field of facial expression recognition were generated
and are summarized in this thesis. It then proposes a model for smile
detection, of which the main part is implemented. The experimental results are
discussed in this thesis and justified based on a comprehensive model selection
performed. All experiments were run on a Tesla K40c GPU benefiting from a
speedup of up to factor 10 over the computations on a CPU. A smile detection
test accuracy of 99.45% is achieved for the Denver Intensity of Spontaneous
Facial Action (DISFA) database, significantly outperforming existing approaches
with accuracies ranging from 65.55% to 79.67%. This experiment is re-run under
various variations, such as retaining less neutral images or only the low or
high intensities, of which the results are extensively compared.
