  The development of facial biometric systems has contributed greatly to the
development of the computer vision field. Nowadays, there's always a need to
develop a multimodal system that combines multiple biometric traits in an
efficient, meaningful way. In this paper, we introduce "IdentiFace" which is a
multimodal facial biometric system that combines the core of facial recognition
with some of the most important soft biometric traits such as gender, face
shape, and emotion. We also focused on developing the system using only VGG-16
inspired architecture with minor changes across different subsystems. This
unification allows for simpler integration across modalities. It makes it
easier to interpret the learned features between the tasks which gives a good
indication about the decision-making process across the facial modalities and
potential connection. For the recognition problem, we acquired a 99.2% test
accuracy for five classes with high intra-class variations using data collected
from the FERET database[1]. We achieved 99.4% on our dataset and 95.15% on the
public dataset[2] in the gender recognition problem. We were also able to
achieve a testing accuracy of 88.03% in the face-shape problem using the
celebrity face-shape dataset[3]. Finally, we achieved a decent testing accuracy
of 66.13% in the emotion task which is considered a very acceptable accuracy
compared to related work on the FER2013 dataset[4].
