Abstract-Emotions have direct influence on the human life and are of great importance in relationships and in the way interactions between individuals develop. Because of this, they are also important for the development of human-machine interfaces that aim to maintain a natural and friendly interaction with its users. In the development of social robots, which this work aims for, a suitable interpretation of the emotional state of the person interacting with the social robot is indispensable. The focus of this paper is the development of a mathematical model for recognizing emotional facial expressions in a sequence of frames. Firstly, a face tracker algorithm is used to find and keep track of faces in images; then the found faces are fed into the model developed in this work, which consists of an instantaneous emotional expression classifier, a Kalman filter and a dynamic classifier that gives the final output of the model.

The aesthetic classification of photographies is a problem of separating aesthetically pleasing images from not pleasing images using algorithms that describe and evaluate both emotional and technical factors. Since the mass adoption of deep convolutional neural network (DCNN) models for image classification problems different DCNN architectures have been developed due to its overall better performance, pushing the boundaries of the state-of-the-art performance of the image classification further. This paper evaluates how architectures and features that were primarily developed for the ImageNet Object Classification Challenge perform when analyzed under the aesthetic scope. A high level transfer learning model composed of a DCNN layer and a top layer that behaves as a linear SVM is proposed and seven different DCNN architectures are trained using it. Scenarios with just transfer learning and with fine tuning are evaluated and a model using the ResNet-Inception V2 architecture is proposed, which achieves results better than current state-of-the-art for the experiment conditions used.

Palavras-chave: aprendizado computacional; redes neurais; reconhecimento de padrões; processamento de imagens; estética(arte).