Deep Neural Network Architecture: Application for Facial Expression Recognition
Moisés García Villanueva, Salvador Ramírez Zavala
Abstract
There are great challenges to build a model or architecture in Deep Learning and integrate it into a real-time application. One of these challenges is the construction or acquisition of large quality datasets (thousands or millions of objects). Another one is to have great computing potential for the architecture learning process. Finally, efficient architectures are needed for the design of deep neural networks, which requires expertise, human experience and practical work. This work presents a deep neural network architecture to classify two feelings of facial expression (happy and sad). A set of data is also created that present great changes in: image environments, facial expression, pose, age, ethnicity and others. The evidence presented shows a competitive architecture and indicates an accuracy greater than 90% with noisy data. Finally, the implementation of a real-time application for facial expression recognition is shown.