Emotion Recognition in the Wild using Deep Neural Networks and Bayesian Classifiers

Group emotion recognition in the wild is a challenging problem, due to the unstructured environments in which everyday life pictures are taken. Some of the obstacles for an effective classification are occlusions, variable lighting conditions, and image quality. In this work we present a solution based on a novel combination of deep neural networks and Bayesian classifiers. The neural network works on a bottom-up approach, analyzing emotions expressed by isolated faces. The Bayesian classifier estimates a global emotion integrating top-down features obtained through a scene descriptor. In order to validate the system we tested the framework on the dataset released for the Emotion Recognition in the Wild Challenge 2017. Our method achieved an accuracy of 64.68% on the test set, significantly outperforming the 53.62% competition baseline.Comment: accepted by the Fifth Emotion Recognition in the Wild (EmotiW) Challenge 201

After-effects and the reach of perceptual content

In this paper, I discuss the use of after-effects as a criterion for showing that we can perceive high-level properties. According to this criterion, if a high-level property is susceptible to after-effects, this suggests that the property can be perceived, rather than cognized. The defenders of the criterion claim that, since after-effects are also present for low-level, uncontroversially perceptual properties, we can safely infer that high-level after-effects are perceptual as well. The critics of the criterion, on the other hand, assimilate it to superficially similar effects in cognition and argue that the after-effect criterion is a cognitive phenomenon rather than a perceptual one, and that as a result it is not a reliable guide for exploring the contents of perception. I argue against both of these views and show that high-level after-effects cannot be identified either with low-level after-effects or with cognitive biases. I suggest an intermediate position: high-level after-effects are not cognitive, but they are nonetheless not a good criterion for exploring the contents of perception