Name: André Teixeira Lopes
Type: MSc dissertation
Publication date: 03/03/2016
Advisor:

Name Rolesort descending
Thiago Oliveira dos Santos Advisor *
Edilson de Aguiar Co-advisor *

Examining board:

Name Rolesort descending
Thiago Oliveira dos Santos Advisor *
Edilson de Aguiar Co advisor *
Siome Klein Goldenstein External Examiner *
Alberto Ferreira De Souza Internal Examiner *

Summary: Facial expression recognition has been an active research area in the past ten years, with growing application areas such avatar animation, neuromarketing and sociable robots.
The recognition of facial expressions is not an easy problem for machine learning methods, since people can vary signicantly in the way that they show their expressions. Even images of the same person in one expression can vary in brightness, background and position. Hence, facial expression recognition is still a challenging problem. To address these problems, in this work we propose a facial expression recognition system that uses Convolutional Neural Networks. Data augmentation and dierent preprocessing
steps were studied together with various Convolutional Neural Networks architectures. The data augmentation and pre-processing steps were used to help the network on the feature selection. Experiments were carried out with three largely used databases (Cohn-Kanade, JAFFE, and BU3DFE) and cross-database validations (i.e. training in one database and test in another) were also performed. The proposed approach has shown to be very eective, improving the state-of-the-art results in the literature and allowing real time facial expression recognition with standard PC computers.

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