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

An Automatic Multilevel Facial Expression Recognition System

Facial expression is one of the most natural way of human beings to communicate his-her internal feeling, to stress his-her words, to agree or disagree with the interlocutor, to regulate interaction with the environment and nearby people. This paper challenges the classification experiment run by human beings on the ADFES-BIV database, which is a recently introduced collection of videos expressing low, middle, and high intensity emotions. The proposed automatic system uses the Sparse Representation based Classifier and reaches the top performance of 80 % by considering the temporal information intrinsically present in the videos

A Survey of Product Recognition in Shelf Images

Melek, Ceren Gülra (Arel Author)Nowadays, merchandising is one of the significant method which allows to increase the sales. Therefore, activities such as monitoring the number of products on the shelves, completing the missing products and matching the planogram continuously have become important. An autonomous system is needed to automate operations such as product or brand recognition, stock tracking and planogram matching. In the literature, it is seen that many studies have been carried out in order to address this issue. This survey classifies and compares all existing works with the aim to guide researchers working on merchandising