  The human face constantly conveys information, both consciously and
subconsciously. However, as basic as it is for humans to visually interpret
this information, it is quite a big challenge for machines. Conventional
semantic facial feature recognition and analysis techniques are already in use
and are based on physiological heuristics, but they suffer from lack of
robustness and high computation time. This thesis aims to explore ways for
machines to learn to interpret semantic information available in faces in an
automated manner without requiring manual design of feature detectors, using
the approach of Deep Learning. This thesis provides a study of the effects of
various factors and hyper-parameters of deep neural networks in the process of
determining an optimal network configuration for the task of semantic facial
feature recognition. This thesis explores the effectiveness of the system to
recognize the various semantic features (like emotions, age, gender, ethnicity
etc.) present in faces. Furthermore, the relation between the effect of
high-level concepts on low level features is explored through an analysis of
the similarities in low-level descriptors of different semantic features. This
thesis also demonstrates a novel idea of using a deep network to generate 3-D
Active Appearance Models of faces from real-world 2-D images.
  For a more detailed report on this work, please see [arXiv:1512.00743v1].
