Abstract:
The deep neural network shows a consequential
performance for a set of specific tasks. A system designed for
some correlated task altogether can be feasible for ‘in the
wild’ applications. This paper proposes a method for the face
localization, Action Unit (AU) and emotion detection. The three
different tasks are performed by a simultaneous hierarchical
network which exploits the way of learning of neural networks.
Such network can represent more relevant features than the
individual network. Due to more complex structures and very
deep networks, the deployment of neural networks for real
life applications is a challenging task. The paper focuses to
find an efficient trade-off between the performance and the
complexity of the given tasks. This is done by exploring the
advantages of optimization of the network for the given tasks
by using separable convolutions, binarization and quantization.
Four different databases (AffectNet, EmotioNet, RAF-DB and
WiderFace) are used to evaluate the performance of our proposed
approach by having a separate task specific database.
